04-CV/06-Segmentation/SemanticSegmentation.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3AbTeDP5Tbou"
      },
      "source": [
        "# Семантическая сегментация\n",
        "\n",
        "**Семантическая сегментация (Semantic Segmentation)** - это по сути задача **пиксельной классификации**, когда для каждого пикселя изображения необходимо указать его класс (включая фон). Семантическая сегментация сообщает только пиксельный класс, более продвинутые алгоритма, находящие экземпляры объектов, называются **Instance Segmentation**. \n",
        "\n",
        "Почти все архитектуры сегментации имеют одинаковую структуру. Первая часть - это **кодировщик**, который извлекает признаки из входного изображения, вторая часть - **декодер**, который преобразует эти признаки обратно в изображение с той же высотой и шириной и некоторым количеством каналов, которое равно количеству классов. Для каждого пикселя исходного изображения мы получаем на выходе вероятность его принадлежности к тому или иному классу."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ug57Bnb5QhfP"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "import tensorflow.keras.layers as keras\n",
        "import matplotlib.pyplot as plt\n",
        "from tqdm import tqdm\n",
        "import numpy as np\n",
        "from skimage.io import imread\n",
        "from skimage.transform import resize\n",
        "import os\n",
        "import tensorflow.keras.optimizers as optimizers\n",
        "import tensorflow.keras.losses as losses\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "tf.random.set_seed(42)\n",
        "np.random.seed(42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xhSEogpDFMpK"
      },
      "outputs": [],
      "source": [
        "train_size = 0.8\n",
        "lr = 3e-4\n",
        "weight_decay = 8e-9\n",
        "batch_size = 64\n",
        "epochs = 100"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D4if75qwFMpJ"
      },
      "source": [
        "## Датасет\n",
        "\n",
        "Используем [датасет фотографий родинок](https://www.kaggle.com/datasets/kanametov/ph2dataset) на теле человека c Kaggle.\n",
        "\n",
        "![](https://raw.githubusercontent.com/microsoft/AI-For-Beginners/main/lessons/4-ComputerVision/12-Segmentation/images/navi.png)\n",
        "\n",
        " Мы загрузим слегка модифицированную его версию:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TkuYGLRKFMpK"
      },
      "outputs": [],
      "source": [
        "!wget http://www.soshnikov.com/permanent/data/PH2Dataset.rar\n",
        "!unrar x -Y PH2Dataset.rar"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "Rumy9ldAFteW"
      },
      "outputs": [],
      "source": [
        "def load_dataset(train_part, root='PH2Dataset'):\n",
        "    images = []\n",
        "    masks = []\n",
        "\n",
        "    for root, dirs, files in os.walk(os.path.join(root, 'PH2 Dataset images')):\n",
        "        if root.endswith('_Dermoscopic_Image'):\n",
        "            images.append(imread(os.path.join(root, files[0])))\n",
        "        if root.endswith('_lesion'):\n",
        "            masks.append(imread(os.path.join(root, files[0])))\n",
        "\n",
        "    size = (256, 256)\n",
        "    images = np.array([resize(image, size, mode='constant', anti_aliasing=True,) for image in images])\n",
        "    masks = np.expand_dims(np.array([resize(mask, size, mode='constant', anti_aliasing=False) > 0.5 for mask in masks]), axis=3)\n",
        "\n",
        "    indices = np.random.permutation(range(len(images)))\n",
        "    train_part = int(train_part * len(images))\n",
        "    train_ind = indices[:train_part]\n",
        "    test_ind = indices[train_part:]\n",
        "\n",
        "    X_train = tf.cast(images[train_ind, :, :, :], tf.float32)\n",
        "    y_train = tf.cast(masks[train_ind, :, :, :], tf.float32)\n",
        "\n",
        "    X_test = tf.cast(images[test_ind, :, :, :], tf.float32)\n",
        "    y_test = tf.cast(masks[test_ind, :, :, :], tf.float32)\n",
        "\n",
        "    return (X_train, y_train), (X_test, y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "kCjj0YKcGBKr"
      },
      "outputs": [],
      "source": [
        "(X_train, y_train), (X_test, y_test) = load_dataset(train_size)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IFEAaR3VILus",
        "outputId": "4febecdc-de23-4b6a-b7bd-ed44083187ad"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(TensorShape([160, 256, 256, 3]), TensorShape([160, 256, 256, 1]))"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X_train.shape, y_train.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "jP2_-AjIFMpO"
      },
      "outputs": [],
      "source": [
        "def plotn(n, data):\n",
        "    images, masks = data[0], data[1]\n",
        "    fig, ax = plt.subplots(1, n)\n",
        "    fig1, ax1 = plt.subplots(1, n)\n",
        "    for i, (img, mask) in enumerate(zip(images, masks)):\n",
        "        if i == n:\n",
        "            break\n",
        "        ax[i].imshow(img)\n",
        "        ax1[i].imshow(mask[:, :, 0])\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9tBja_1KFMpP"
      },
      "source": [
        "Посмотрим на изображения из датасета:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "IA8ziDUJFMpQ",
        "outputId": "ec5f00ef-e013-4d0c-fbab-912141808966"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 5 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 5 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plotn(5, (X_train, y_train))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ORmas8XhYfS8"
      },
      "source": [
        "## SegNet\n",
        "\n",
        "SegNet - это самая простая архитектура энкодера-декодера, в которой CNN-энкодер сначала переводит изображение в вектор признаков, а затем по нему восстанавливает маску.\n",
        "\n",
        "<img src=\"./images/segnet.png\" width=\"80%\">\n",
        "\n",
        "* Badrinarayanan, V., Kendall, A., & Cipolla, R. (2015). [SegNet: A deep convolutional encoder-decoder architecture for image segmentation](https://arxiv.org/pdf/1511.00561.pdf)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pmSOZXhfp6sL"
      },
      "outputs": [],
      "source": [
        "class SegNet(tf.keras.Model):\n",
        "    def __init__(self):\n",
        "        super().__init__()\n",
        "        self.enc_conv0 = keras.Conv2D(16, kernel_size=3, padding='same')\n",
        "        self.bn0 = keras.BatchNormalization()\n",
        "        self.pool0 = keras.MaxPool2D()\n",
        "\n",
        "        self.enc_conv1 = keras.Conv2D(32, kernel_size=3, padding='same')\n",
        "        self.bn1 = keras.BatchNormalization()\n",
        "        self.pool1 = keras.MaxPool2D()\n",
        "\n",
        "        self.enc_conv2 = keras.Conv2D(64, kernel_size=3, padding='same')\n",
        "        self.bn2 = keras.BatchNormalization()\n",
        "        self.pool2 = keras.MaxPool2D()\n",
        "\n",
        "        self.enc_conv3 = keras.Conv2D(128, kernel_size=3, padding='same')\n",
        "        self.bn3 = keras.BatchNormalization()\n",
        "        self.pool3 = keras.MaxPool2D()\n",
        "\n",
        "        self.bottleneck_conv = keras.Conv2D(256, kernel_size=(3, 3), padding='same')\n",
        "\n",
        "        self.upsample0 =  keras.UpSampling2D(interpolation='bilinear')\n",
        "        self.dec_conv0 = keras.Conv2D(128, kernel_size=3, padding='same')\n",
        "        self.dec_bn0 = keras.BatchNormalization()\n",
        "\n",
        "        self.upsample1 =  keras.UpSampling2D(interpolation='bilinear')\n",
        "        self.dec_conv1 = keras.Conv2D(64, kernel_size=3, padding='same')\n",
        "        self.dec_bn1 = keras.BatchNormalization()\n",
        "\n",
        "        self.upsample2 =  keras.UpSampling2D(interpolation='bilinear')\n",
        "        self.dec_conv2 = keras.Conv2D(32, kernel_size=3, padding='same')\n",
        "        self.dec_relu2 = keras.Activation('relu')\n",
        "        self.dec_bn2 = keras.BatchNormalization()\n",
        "\n",
        "        self.upsample3 =  keras.UpSampling2D(interpolation='bilinear')\n",
        "        self.dec_conv3 = keras.Conv2D(1, kernel_size=1)\n",
        "\n",
        "    def call(self, input):\n",
        "        e0 = self.pool0(tf.nn.relu(self.bn0(self.enc_conv0(input))))\n",
        "        e1 = self.pool1(tf.nn.relu(self.bn1(self.enc_conv1(e0))))\n",
        "        e2 = self.pool2(tf.nn.relu(self.bn2(self.enc_conv2(e1))))\n",
        "        e3 = self.pool3(tf.nn.relu(self.bn3(self.enc_conv3(e2))))\n",
        "\n",
        "        b = self.bottleneck_conv(e3)\n",
        "\n",
        "        d0 = tf.nn.relu(self.dec_bn0(self.upsample0(self.dec_conv0(b))))\n",
        "        d1 = tf.nn.relu(self.dec_bn1(self.upsample1(self.dec_conv1(d0))))\n",
        "        d2 = tf.nn.relu(self.dec_bn2(self.upsample2(self.dec_conv2(d1))))\n",
        "        d3 = self.dec_conv3(self.upsample3(d2))\n",
        "\n",
        "        return d3"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "2GoR8-huFMpn"
      },
      "outputs": [],
      "source": [
        "model = SegNet()\n",
        "optimizer = optimizers.Adam(learning_rate=lr, decay=weight_decay)\n",
        "loss_fn = losses.BinaryCrossentropy(from_logits=True)\n",
        "\n",
        "model.compile(loss=loss_fn, optimizer=optimizer)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HQ_UFglyTbqM",
        "outputId": "50e1ca9c-3de0-4488-cb64-c51b9f6201cd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/30\n",
            "3/3 [==============================] - 1s 262ms/step - loss: 0.0577 - val_loss: 0.4028\n",
            "Epoch 2/30\n",
            "3/3 [==============================] - 1s 240ms/step - loss: 0.0578 - val_loss: 0.4238\n",
            "Epoch 3/30\n",
            "3/3 [==============================] - 1s 194ms/step - loss: 0.0554 - val_loss: 0.4070\n",
            "Epoch 4/30\n",
            "3/3 [==============================] - 1s 196ms/step - loss: 0.0553 - val_loss: 0.4162\n",
            "Epoch 5/30\n",
            "3/3 [==============================] - 1s 194ms/step - loss: 0.0555 - val_loss: 0.3997\n",
            "Epoch 6/30\n",
            "3/3 [==============================] - 1s 194ms/step - loss: 0.0542 - val_loss: 0.3847\n",
            "Epoch 7/30\n",
            "3/3 [==============================] - 1s 196ms/step - loss: 0.0560 - val_loss: 0.3629\n",
            "Epoch 8/30\n",
            "3/3 [==============================] - 1s 199ms/step - loss: 0.0520 - val_loss: 0.3761\n",
            "Epoch 9/30\n",
            "3/3 [==============================] - 1s 194ms/step - loss: 0.0543 - val_loss: 0.3601\n",
            "Epoch 10/30\n",
            "3/3 [==============================] - 1s 197ms/step - loss: 0.0546 - val_loss: 0.3460\n",
            "Epoch 11/30\n",
            "3/3 [==============================] - 1s 192ms/step - loss: 0.0540 - val_loss: 0.3371\n",
            "Epoch 12/30\n",
            "3/3 [==============================] - 1s 195ms/step - loss: 0.0556 - val_loss: 0.3590\n",
            "Epoch 13/30\n",
            "3/3 [==============================] - 1s 195ms/step - loss: 0.0489 - val_loss: 0.3354\n",
            "Epoch 14/30\n",
            "3/3 [==============================] - 1s 199ms/step - loss: 0.0515 - val_loss: 0.3105\n",
            "Epoch 15/30\n",
            "3/3 [==============================] - 1s 191ms/step - loss: 0.0482 - val_loss: 0.3090\n",
            "Epoch 16/30\n",
            "3/3 [==============================] - 1s 195ms/step - loss: 0.0483 - val_loss: 0.3330\n",
            "Epoch 17/30\n",
            "3/3 [==============================] - 1s 199ms/step - loss: 0.0466 - val_loss: 0.3167\n",
            "Epoch 18/30\n",
            "3/3 [==============================] - 1s 197ms/step - loss: 0.0464 - val_loss: 0.3006\n",
            "Epoch 19/30\n",
            "3/3 [==============================] - 1s 196ms/step - loss: 0.0553 - val_loss: 0.3077\n",
            "Epoch 20/30\n",
            "3/3 [==============================] - 1s 195ms/step - loss: 0.0474 - val_loss: 0.2929\n",
            "Epoch 21/30\n",
            "3/3 [==============================] - 1s 200ms/step - loss: 0.0494 - val_loss: 0.2773\n",
            "Epoch 22/30\n",
            "3/3 [==============================] - 1s 198ms/step - loss: 0.0478 - val_loss: 0.2774\n",
            "Epoch 23/30\n",
            "3/3 [==============================] - 1s 195ms/step - loss: 0.0491 - val_loss: 0.2649\n",
            "Epoch 24/30\n",
            "3/3 [==============================] - 1s 197ms/step - loss: 0.0483 - val_loss: 0.2757\n",
            "Epoch 25/30\n",
            "3/3 [==============================] - 1s 201ms/step - loss: 0.0470 - val_loss: 0.2750\n",
            "Epoch 26/30\n",
            "3/3 [==============================] - 1s 197ms/step - loss: 0.0490 - val_loss: 0.2595\n",
            "Epoch 27/30\n",
            "3/3 [==============================] - 1s 197ms/step - loss: 0.0554 - val_loss: 0.2638\n",
            "Epoch 28/30\n",
            "3/3 [==============================] - 1s 197ms/step - loss: 0.0471 - val_loss: 0.2540\n",
            "Epoch 29/30\n",
            "3/3 [==============================] - 1s 194ms/step - loss: 0.0444 - val_loss: 0.2389\n",
            "Epoch 30/30\n",
            "3/3 [==============================] - 1s 188ms/step - loss: 0.0447 - val_loss: 0.2583\n"
          ]
        }
      ],
      "source": [
        "hist = model.fit(X_train, y_train,\n",
        "                epochs=30,\n",
        "                batch_size=batch_size,\n",
        "                shuffle=True,\n",
        "                validation_data=(X_test, y_test))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        },
        "id": "FXvR7P1FFMpo",
        "outputId": "dd0505c5-2e91-459f-f16b-67a76250cf5b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 5 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 5 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "predictions = []\n",
        "image_mask = []\n",
        "plots = 5\n",
        "\n",
        "for i, (img, mask) in enumerate(zip(X_test, y_test)):\n",
        "    if i == plots:\n",
        "        break\n",
        "    img = tf.expand_dims(img, 0)\n",
        "    pred = np.array(model.predict(img))\n",
        "    predictions.append(pred[0, :, :, 0] > 0.5)\n",
        "    image_mask.append(mask)\n",
        "plotn(plots, (predictions, image_mask))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RU5KGWXaTbso"
      },
      "source": [
        "## U-Net\n",
        "\n",
        "Основная проблема SegNet в том, что, переходя к вектору признаков, мы теряем в точности пространственной информации. Например, в VGG-16, исходное изображение 224x224 преобразуется к тензору 7x7x512, у которого всего лишь 7x7 пространственных измерений, по который в дальнейшем надо восстановить маску. Отчасти проблема решается в SegNet наличием Pooling Indices, а отчасти тем, что фильтры CNN способны \"помнить форму\".\n",
        "\n",
        "Однако улучшить сегментацию позволяет подход, при котором на каждом уровне деконволюции мы используем не только информацию с предыдушего уровня, но и информацию с энкодера соответствующего слоя. Такое использование достигается простой конкатенацией тензоров признаков по размерности фильтров (пространственные размерности при этом совпадают).\n",
        "\n",
        "В реализации \"взрослого\" U-Net обычно используют предобученные CNN для энкодера, например, ResNet-50.\n",
        "\n",
        "<img src=\"images/unet.png\" width=\"70%\">\n",
        "\n",
        "* Ronneberger, Olaf, Philipp Fischer, and Thomas Brox. [U-Net: Convolutional networks for biomedical image segmentation.](https://arxiv.org/pdf/1505.04597.pdf)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "dFFh1_UYQc-K"
      },
      "outputs": [],
      "source": [
        "class UNet(tf.keras.Model):\n",
        "    def __init__(self):\n",
        "        super().__init__()\n",
        "        self.enc_conv0 = keras.Conv2D(16, kernel_size=3, padding='same')\n",
        "        self.bn0 = keras.BatchNormalization()\n",
        "        self.pool0 = keras.MaxPool2D()\n",
        "\n",
        "        self.enc_conv1 = keras.Conv2D(32, kernel_size=3, padding='same')\n",
        "        self.bn1 = keras.BatchNormalization()\n",
        "        self.pool1 = keras.MaxPool2D()\n",
        "\n",
        "        self.enc_conv2 = keras.Conv2D(64, kernel_size=3, padding='same')\n",
        "        self.bn2 = keras.BatchNormalization()\n",
        "        self.pool2 = keras.MaxPool2D()\n",
        "\n",
        "        self.enc_conv3 = keras.Conv2D(128, kernel_size=3, padding='same')\n",
        "        self.bn3 = keras.BatchNormalization()\n",
        "        self.pool3 = keras.MaxPool2D()\n",
        "\n",
        "        self.bottleneck_conv = keras.Conv2D(256, kernel_size=(3, 3), padding='same')\n",
        "\n",
        "        self.upsample0 =  keras.UpSampling2D(interpolation='bilinear')\n",
        "        self.dec_conv0 = keras.Conv2D(128, kernel_size=3, padding='same', input_shape=[None, 384, None, None])\n",
        "        self.dec_bn0 = keras.BatchNormalization()\n",
        "\n",
        "        self.upsample1 =  keras.UpSampling2D(interpolation='bilinear')\n",
        "        self.dec_conv1 = keras.Conv2D(64, kernel_size=3, padding='same', input_shape=[None, 192, None, None])\n",
        "        self.dec_bn1 = keras.BatchNormalization()\n",
        "\n",
        "        self.upsample2 =  keras.UpSampling2D(interpolation='bilinear')\n",
        "        self.dec_conv2 = keras.Conv2D(32, kernel_size=3, padding='same', input_shape=[None, 96, None, None])\n",
        "        self.dec_bn2 = keras.BatchNormalization()\n",
        "\n",
        "        self.upsample3 =  keras.UpSampling2D(interpolation='bilinear')\n",
        "        self.dec_conv3 = keras.Conv2D(1, kernel_size=1, input_shape=[None, 48, None, None])\n",
        "\n",
        "    def call(self, input):\n",
        "        cat0 = tf.nn.relu(self.bn0(self.enc_conv0(input)))\n",
        "        e0 = self.pool0(cat0)\n",
        "        cat1 = tf.nn.relu(self.bn1(self.enc_conv1(e0)))\n",
        "        e1 = self.pool0(cat1)\n",
        "        cat2 = tf.nn.relu(self.bn2(self.enc_conv2(e1)))\n",
        "        e2 = self.pool0(cat2)\n",
        "        cat3 = tf.nn.relu(self.bn3(self.enc_conv3(e2)))\n",
        "        e3 = self.pool0(cat3)\n",
        "\n",
        "        b = self.bottleneck_conv(e3)\n",
        "\n",
        "        cat_tens0 = tf.concat([self.upsample0(b), cat3],axis=3)\n",
        "        d0 = tf.nn.relu(self.dec_bn0(self.dec_conv0(cat_tens0)))\n",
        "\n",
        "        cat_tens1 = tf.concat([self.upsample1(d0), cat2],axis=3)\n",
        "        d1 = tf.nn.relu(self.dec_bn1(self.dec_conv1(cat_tens1)))\n",
        "\n",
        "        cat_tens2 = tf.concat([self.upsample2(d1), cat1],axis=3)\n",
        "        d2 = tf.nn.relu(self.dec_bn2(self.dec_conv2(cat_tens2)))\n",
        "\n",
        "        cat_tens3 = tf.concat([self.upsample3(d2), cat0],axis=3)\n",
        "        d3 = self.dec_conv3(cat_tens3)\n",
        "\n",
        "        return d3"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "15GA_43BTbtI"
      },
      "outputs": [],
      "source": [
        "model = UNet()\n",
        "optimizer = optimizers.Adam(learning_rate=lr, decay=weight_decay)\n",
        "loss_fn = losses.BinaryCrossentropy(from_logits=True)\n",
        "\n",
        "model.compile(loss=loss_fn, optimizer=optimizer)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_dgiuvVVFMpr",
        "outputId": "095721e1-5090-41b4-92a1-6dc9fe9d31cb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/30\n",
            "3/3 [==============================] - 1s 311ms/step - loss: 0.0773 - val_loss: 0.4684\n",
            "Epoch 2/30\n",
            "3/3 [==============================] - 1s 298ms/step - loss: 0.0776 - val_loss: 0.4388\n",
            "Epoch 3/30\n",
            "3/3 [==============================] - 1s 298ms/step - loss: 0.0766 - val_loss: 0.3828\n",
            "Epoch 4/30\n",
            "3/3 [==============================] - 1s 298ms/step - loss: 0.0746 - val_loss: 0.3767\n",
            "Epoch 5/30\n",
            "3/3 [==============================] - 1s 300ms/step - loss: 0.0755 - val_loss: 0.3956\n",
            "Epoch 6/30\n",
            "3/3 [==============================] - 1s 304ms/step - loss: 0.0739 - val_loss: 0.3925\n",
            "Epoch 7/30\n",
            "3/3 [==============================] - 1s 298ms/step - loss: 0.0765 - val_loss: 0.3539\n",
            "Epoch 8/30\n",
            "3/3 [==============================] - 1s 299ms/step - loss: 0.0719 - val_loss: 0.3612\n",
            "Epoch 9/30\n",
            "3/3 [==============================] - 1s 303ms/step - loss: 0.0728 - val_loss: 0.3986\n",
            "Epoch 10/30\n",
            "3/3 [==============================] - 1s 299ms/step - loss: 0.0758 - val_loss: 0.3535\n",
            "Epoch 11/30\n",
            "3/3 [==============================] - 1s 296ms/step - loss: 0.0770 - val_loss: 0.3187\n",
            "Epoch 12/30\n",
            "3/3 [==============================] - 1s 306ms/step - loss: 0.0746 - val_loss: 0.3452\n",
            "Epoch 13/30\n",
            "3/3 [==============================] - 1s 299ms/step - loss: 0.0704 - val_loss: 0.3315\n",
            "Epoch 14/30\n",
            "3/3 [==============================] - 1s 308ms/step - loss: 0.0721 - val_loss: 0.3409\n",
            "Epoch 15/30\n",
            "3/3 [==============================] - 1s 303ms/step - loss: 0.0700 - val_loss: 0.3061\n",
            "Epoch 16/30\n",
            "3/3 [==============================] - 1s 356ms/step - loss: 0.0691 - val_loss: 0.3088\n",
            "Epoch 17/30\n",
            "3/3 [==============================] - 1s 337ms/step - loss: 0.0676 - val_loss: 0.3215\n",
            "Epoch 18/30\n",
            "3/3 [==============================] - 1s 321ms/step - loss: 0.0673 - val_loss: 0.2715\n",
            "Epoch 19/30\n",
            "3/3 [==============================] - 1s 314ms/step - loss: 0.0742 - val_loss: 0.2857\n",
            "Epoch 20/30\n",
            "3/3 [==============================] - 1s 304ms/step - loss: 0.0649 - val_loss: 0.2546\n",
            "Epoch 21/30\n",
            "3/3 [==============================] - 1s 302ms/step - loss: 0.0672 - val_loss: 0.2525\n",
            "Epoch 22/30\n",
            "3/3 [==============================] - 1s 323ms/step - loss: 0.0659 - val_loss: 0.2534\n",
            "Epoch 23/30\n",
            "3/3 [==============================] - 1s 299ms/step - loss: 0.0705 - val_loss: 0.2804\n",
            "Epoch 24/30\n",
            "3/3 [==============================] - 1s 305ms/step - loss: 0.0685 - val_loss: 0.2432\n",
            "Epoch 25/30\n",
            "3/3 [==============================] - 1s 302ms/step - loss: 0.0654 - val_loss: 0.2386\n",
            "Epoch 26/30\n",
            "3/3 [==============================] - 1s 304ms/step - loss: 0.0713 - val_loss: 0.2574\n",
            "Epoch 27/30\n",
            "3/3 [==============================] - 1s 300ms/step - loss: 0.0733 - val_loss: 0.2272\n",
            "Epoch 28/30\n",
            "3/3 [==============================] - 1s 305ms/step - loss: 0.0655 - val_loss: 0.2127\n",
            "Epoch 29/30\n",
            "3/3 [==============================] - 1s 302ms/step - loss: 0.0602 - val_loss: 0.2144\n",
            "Epoch 30/30\n",
            "3/3 [==============================] - 1s 298ms/step - loss: 0.0609 - val_loss: 0.2238\n"
          ]
        }
      ],
      "source": [
        "hist = model.fit(X_train, y_train,\n",
        "                epochs=30,\n",
        "                batch_size=batch_size,\n",
        "                shuffle=True,\n",
        "                validation_data=(X_test, y_test))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "SI-n-Q0ZMcCp",
        "outputId": "bc9dae1d-401a-4e8c-9932-090d28178323"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7ff32810dd10>]"
            ]
          },
          "execution_count": 40,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.plot(hist.history['loss'])\n",
        "plt.plot(hist.history['val_loss'])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        },
        "id": "iEu5wjuMFMps",
        "outputId": "7f5fc400-60c7-460a-daf8-35dda4c4bb12"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 5 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 5 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "predictions = []\n",
        "image_mask = []\n",
        "plots = 5\n",
        "\n",
        "for i, (img, mask) in enumerate(zip(X_test, y_test)):\n",
        "    if i == plots:\n",
        "        break\n",
        "    img = tf.expand_dims(img, 0)\n",
        "    pred = np.array(model.predict(img))\n",
        "    predictions.append(pred[0, :, :, 0] > 0.5)\n",
        "    image_mask.append(mask)\n",
        "plotn(plots, (predictions, image_mask))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "l8-p2La8TlKr"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3.9.12 ('base')",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.12"
    },
    "vscode": {
      "interpreter": {
        "hash": "d21c24a7952cc9fa93a33c3da71b373ffe761d6ab0c9cc9b2153e31da51ffb58"
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}