damage_segmentations_weight_evo_sec (4).ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "zWCqLErX8Tkd",
        "outputId": "5a6ca8f3-76cc-49bf-f010-3a678ff0a9a1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting roboflow\n",
            "  Downloading roboflow-1.1.45-py3-none-any.whl.metadata (9.7 kB)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from roboflow) (2024.8.30)\n",
            "Collecting idna==3.7 (from roboflow)\n",
            "  Downloading idna-3.7-py3-none-any.whl.metadata (9.9 kB)\n",
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            "\u001b[?25hInstalling collected packages: filetype, python-dotenv, idna, requests-toolbelt, roboflow\n",
            "  Attempting uninstall: idna\n",
            "    Found existing installation: idna 3.10\n",
            "    Uninstalling idna-3.10:\n",
            "      Successfully uninstalled idna-3.10\n",
            "Successfully installed filetype-1.2.0 idna-3.7 python-dotenv-1.0.1 requests-toolbelt-1.0.0 roboflow-1.1.45\n"
          ]
        }
      ],
      "source": [
        "!pip install roboflow"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "7AuJYg9B9EIG",
        "outputId": "5f20aea0-d669-4aa7-85cf-36783913e695"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting ultralytics==8.0.196\n",
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            "Collecting thop>=0.1.1 (from ultralytics==8.0.196)\n",
            "  Downloading thop-0.1.1.post2209072238-py3-none-any.whl.metadata (2.7 kB)\n",
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            "Installing collected packages: thop, ultralytics\n",
            "Successfully installed thop-0.1.1.post2209072238 ultralytics-8.0.196\n"
          ]
        }
      ],
      "source": [
        "!pip install ultralytics==8.0.196"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "Use your roboflow API key here\n",
        "\"\"\"\n",
        "\n",
        "API_KEY = 'XXXXXXXXXXXXXXXXXXXXXXXXX'"
      ],
      "metadata": {
        "id": "CDB6k6X91GQj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_7R2xXw18Wa8",
        "outputId": "b2206e54-6d03-4d48-859e-4640b22b6b3a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "loading Roboflow workspace...\n",
            "loading Roboflow project...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading Dataset Version Zip in walls-1 to yolov8:: 100%|██████████| 31545/31545 [00:03<00:00, 10502.97it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "Extracting Dataset Version Zip to walls-1 in yolov8:: 100%|██████████| 2226/2226 [00:00<00:00, 2834.79it/s]\n"
          ]
        }
      ],
      "source": [
        "\n",
        "from roboflow import Roboflow\n",
        "rf = Roboflow(api_key= API_KEY)\n",
        "project = rf.workspace(\"workspace-2-iz9jz\").project(\"walls-2tpff\")\n",
        "version = project.version(1)\n",
        "dataset = version.download(\"yolov8\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7g6emCg08Y3f"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import cv2\n",
        "import numpy as np\n",
        "import os\n",
        "from tensorflow.keras.callbacks import ModelCheckpoint\n",
        "\n",
        "# Google Drive best weight path\n",
        "# Define paths\n",
        "BEST_WEIGHTS_PATH = '/content/drive/My Drive/best_weights.h5'\n",
        "base_dir = 'walls-1'\n",
        "label_dirs = ['train/labels', 'valid/labels', 'test/labels']\n",
        "image_dirs = ['train/images', 'valid/images', 'test/images']\n",
        "mask_dirs = ['train/masks', 'valid/masks', 'test/masks']\n",
        "\n",
        "# Create mask directories if they don't exist\n",
        "for mask_dir in mask_dirs:\n",
        "    os.makedirs(os.path.join(base_dir, mask_dir), exist_ok=True)\n",
        "\n",
        "# Function to create masks\n",
        "def create_masks(label_dir, image_dir, mask_dir):\n",
        "    label_path = os.path.join(base_dir, label_dir)\n",
        "    image_path = os.path.join(base_dir, image_dir)\n",
        "    mask_path = os.path.join(base_dir, mask_dir)\n",
        "\n",
        "    for label_file in os.listdir(label_path):\n",
        "        if label_file.endswith('.txt'):\n",
        "            # Read the corresponding image\n",
        "            image_file = label_file.replace('.txt', '.jpg')\n",
        "            image = cv2.imread(os.path.join(image_path, image_file))\n",
        "            if image is None:\n",
        "                continue\n",
        "\n",
        "            height, width, _ = image.shape\n",
        "            mask = np.zeros((height, width), dtype=np.uint8)\n",
        "\n",
        "            # Read the label file\n",
        "            with open(os.path.join(label_path, label_file), 'r') as file:\n",
        "                for line in file:\n",
        "                    parts = line.strip().split()\n",
        "                    class_id = int(parts[0])\n",
        "                    x_center = float(parts[1]) * width\n",
        "                    y_center = float(parts[2]) * height\n",
        "                    bbox_width = float(parts[3]) * width\n",
        "                    bbox_height = float(parts[4]) * height\n",
        "\n",
        "                    x_min = int(x_center - bbox_width / 2)\n",
        "                    y_min = int(y_center - bbox_height / 2)\n",
        "                    x_max = int(x_center + bbox_width / 2)\n",
        "                    y_max = int(y_center + bbox_height / 2)\n",
        "\n",
        "                    # Draw the bounding box on the mask\n",
        "                    cv2.rectangle(mask, (x_min, y_min), (x_max, y_max), (255), thickness=-1)\n",
        "\n",
        "            # Save the mask\n",
        "            mask_file = label_file.replace('.txt', '.png')\n",
        "            cv2.imwrite(os.path.join(mask_path, mask_file), mask)\n",
        "\n",
        "# Create masks for each dataset split\n",
        "for label_dir, image_dir, mask_dir in zip(label_dirs, image_dirs, mask_dirs):\n",
        "    create_masks(label_dir, image_dir, mask_dir)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vyJbPptr8Y1H"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import numpy as np\n",
        "import cv2\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, Conv2DTranspose, concatenate, Dropout\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from sklearn.model_selection import train_test_split\n",
        "import matplotlib.pyplot as plt\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2LYpSEjW8Yy4"
      },
      "outputs": [],
      "source": [
        "def load_images_and_masks(image_dir, mask_dir, image_size=(128, 128)):\n",
        "    images = []\n",
        "    masks = []\n",
        "\n",
        "    for image_file in os.listdir(image_dir):\n",
        "        image_path = os.path.join(image_dir, image_file)\n",
        "        mask_path = os.path.join(mask_dir, image_file.replace('.jpg', '.png'))\n",
        "\n",
        "        if os.path.exists(mask_path):\n",
        "            image = cv2.imread(image_path)\n",
        "            image = cv2.resize(image, image_size)\n",
        "            mask = cv2.imread(mask_path, cv2.IMREAD_GRAYSCALE)\n",
        "            mask = cv2.resize(mask, image_size)\n",
        "\n",
        "            images.append(image)\n",
        "            masks.append(mask)\n",
        "\n",
        "    images = np.array(images) / 255.0\n",
        "    masks = np.array(masks) / 255.0\n",
        "    masks = np.expand_dims(masks, axis=-1)\n",
        "    return images, masks\n",
        "\n",
        "train_images, train_masks = load_images_and_masks(os.path.join(base_dir, 'train/images'), os.path.join(base_dir, 'train/masks'))\n",
        "valid_images, valid_masks = load_images_and_masks(os.path.join(base_dir, 'valid/images'), os.path.join(base_dir, 'valid/masks'))\n",
        "test_images, test_masks = load_images_and_masks(os.path.join(base_dir, 'test/images'), os.path.join(base_dir, 'test/masks'))\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import numpy as np\n",
        "import cv2\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, UpSampling2D, concatenate\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.applications import VGG16"
      ],
      "metadata": {
        "id": "aW1Mp1mpuuSu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "4oKvQzM08Ywg",
        "outputId": "719680c1-0d27-470f-f386-2249e07aadf5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "\u001b[1m58889256/58889256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 0us/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"functional\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"functional\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)             \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m       Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to          \u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
              "│ input_layer (\u001b[38;5;33mInputLayer\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m3\u001b[0m)    │              \u001b[38;5;34m0\u001b[0m │ -                      │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block1_conv1 (\u001b[38;5;33mConv2D\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m64\u001b[0m)   │          \u001b[38;5;34m1,792\u001b[0m │ input_layer[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]      │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block1_conv2 (\u001b[38;5;33mConv2D\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m64\u001b[0m)   │         \u001b[38;5;34m36,928\u001b[0m │ block1_conv1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]     │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block1_pool               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │              \u001b[38;5;34m0\u001b[0m │ block1_conv2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]     │\n",
              "│ (\u001b[38;5;33mMaxPooling2D\u001b[0m)            │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block2_conv1 (\u001b[38;5;33mConv2D\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │         \u001b[38;5;34m73,856\u001b[0m │ block1_pool[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]      │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block2_conv2 (\u001b[38;5;33mConv2D\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │        \u001b[38;5;34m147,584\u001b[0m │ block2_conv1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]     │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block2_pool               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │              \u001b[38;5;34m0\u001b[0m │ block2_conv2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]     │\n",
              "│ (\u001b[38;5;33mMaxPooling2D\u001b[0m)            │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block3_conv1 (\u001b[38;5;33mConv2D\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │        \u001b[38;5;34m295,168\u001b[0m │ block2_pool[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]      │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block3_conv2 (\u001b[38;5;33mConv2D\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │        \u001b[38;5;34m590,080\u001b[0m │ block3_conv1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]     │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block3_conv3 (\u001b[38;5;33mConv2D\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │        \u001b[38;5;34m590,080\u001b[0m │ block3_conv2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]     │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block3_pool               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │              \u001b[38;5;34m0\u001b[0m │ block3_conv3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]     │\n",
              "│ (\u001b[38;5;33mMaxPooling2D\u001b[0m)            │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block4_conv1 (\u001b[38;5;33mConv2D\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │      \u001b[38;5;34m1,180,160\u001b[0m │ block3_pool[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]      │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block4_conv2 (\u001b[38;5;33mConv2D\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │      \u001b[38;5;34m2,359,808\u001b[0m │ block4_conv1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]     │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block4_conv3 (\u001b[38;5;33mConv2D\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │      \u001b[38;5;34m2,359,808\u001b[0m │ block4_conv2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]     │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block4_pool               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m512\u001b[0m)      │              \u001b[38;5;34m0\u001b[0m │ block4_conv3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]     │\n",
              "│ (\u001b[38;5;33mMaxPooling2D\u001b[0m)            │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block5_conv1 (\u001b[38;5;33mConv2D\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m512\u001b[0m)      │      \u001b[38;5;34m2,359,808\u001b[0m │ block4_pool[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]      │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block5_conv2 (\u001b[38;5;33mConv2D\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m512\u001b[0m)      │      \u001b[38;5;34m2,359,808\u001b[0m │ block5_conv1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]     │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block5_conv3 (\u001b[38;5;33mConv2D\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m512\u001b[0m)      │      \u001b[38;5;34m2,359,808\u001b[0m │ block5_conv2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]     │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ up_sampling2d             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │              \u001b[38;5;34m0\u001b[0m │ block5_conv3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]     │\n",
              "│ (\u001b[38;5;33mUpSampling2D\u001b[0m)            │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ concatenate (\u001b[38;5;33mConcatenate\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m1024\u001b[0m)   │              \u001b[38;5;34m0\u001b[0m │ up_sampling2d[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],   │\n",
              "│                           │                        │                │ block4_conv3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]     │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2d (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │      \u001b[38;5;34m4,719,104\u001b[0m │ concatenate[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]      │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │      \u001b[38;5;34m2,359,808\u001b[0m │ conv2d[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]           │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ up_sampling2d_1           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │              \u001b[38;5;34m0\u001b[0m │ conv2d_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]         │\n",
              "│ (\u001b[38;5;33mUpSampling2D\u001b[0m)            │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ concatenate_1             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m768\u001b[0m)    │              \u001b[38;5;34m0\u001b[0m │ up_sampling2d_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n",
              "│ (\u001b[38;5;33mConcatenate\u001b[0m)             │                        │                │ block3_conv3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]     │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2d_2 (\u001b[38;5;33mConv2D\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │      \u001b[38;5;34m1,769,728\u001b[0m │ concatenate_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2d_3 (\u001b[38;5;33mConv2D\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │        \u001b[38;5;34m590,080\u001b[0m │ conv2d_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]         │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ up_sampling2d_2           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │              \u001b[38;5;34m0\u001b[0m │ conv2d_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]         │\n",
              "│ (\u001b[38;5;33mUpSampling2D\u001b[0m)            │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ concatenate_2             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m384\u001b[0m)    │              \u001b[38;5;34m0\u001b[0m │ up_sampling2d_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n",
              "│ (\u001b[38;5;33mConcatenate\u001b[0m)             │                        │                │ block2_conv2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]     │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2d_4 (\u001b[38;5;33mConv2D\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │        \u001b[38;5;34m442,496\u001b[0m │ concatenate_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2d_5 (\u001b[38;5;33mConv2D\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │        \u001b[38;5;34m147,584\u001b[0m │ conv2d_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]         │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ up_sampling2d_3           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m)  │              \u001b[38;5;34m0\u001b[0m │ conv2d_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]         │\n",
              "│ (\u001b[38;5;33mUpSampling2D\u001b[0m)            │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ concatenate_3             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m192\u001b[0m)  │              \u001b[38;5;34m0\u001b[0m │ up_sampling2d_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n",
              "│ (\u001b[38;5;33mConcatenate\u001b[0m)             │                        │                │ block1_conv2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]     │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2d_6 (\u001b[38;5;33mConv2D\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m64\u001b[0m)   │        \u001b[38;5;34m110,656\u001b[0m │ concatenate_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2d_7 (\u001b[38;5;33mConv2D\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m64\u001b[0m)   │         \u001b[38;5;34m36,928\u001b[0m │ conv2d_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]         │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2d_8 (\u001b[38;5;33mConv2D\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m1\u001b[0m)    │             \u001b[38;5;34m65\u001b[0m │ conv2d_7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]         │\n",
              "└───────────────────────────┴────────────────────────┴────────────────┴────────────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)              </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">        Param # </span>┃<span style=\"font-weight: bold\"> Connected to           </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
              "│ input_layer (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)    │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                      │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block1_conv1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)   │          <span style=\"color: #00af00; text-decoration-color: #00af00\">1,792</span> │ input_layer[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]      │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block1_conv2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)   │         <span style=\"color: #00af00; text-decoration-color: #00af00\">36,928</span> │ block1_conv1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]     │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block1_pool               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block1_conv2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]     │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)            │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block2_conv1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │         <span style=\"color: #00af00; text-decoration-color: #00af00\">73,856</span> │ block1_pool[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]      │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block2_conv2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">147,584</span> │ block2_conv1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]     │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block2_pool               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block2_conv2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]     │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)            │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block3_conv1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">295,168</span> │ block2_pool[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]      │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block3_conv2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">590,080</span> │ block3_conv1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]     │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block3_conv3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">590,080</span> │ block3_conv2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]     │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block3_pool               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block3_conv3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]     │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)            │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block4_conv1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">1,180,160</span> │ block3_pool[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]      │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block4_conv2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │ block4_conv1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]     │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block4_conv3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │ block4_conv2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]     │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block4_pool               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)      │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block4_conv3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]     │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)            │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block5_conv1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)      │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │ block4_pool[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]      │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block5_conv2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)      │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │ block5_conv1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]     │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ block5_conv3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)      │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │ block5_conv2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]     │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ up_sampling2d             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block5_conv3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]     │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">UpSampling2D</span>)            │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ concatenate (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Concatenate</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)   │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ up_sampling2d[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],   │\n",
              "│                           │                        │                │ block4_conv3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]     │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">4,719,104</span> │ concatenate[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]      │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │ conv2d[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]           │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ up_sampling2d_1           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]         │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">UpSampling2D</span>)            │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ concatenate_1             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">768</span>)    │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ up_sampling2d_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>], │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Concatenate</span>)             │                        │                │ block3_conv3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]     │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">1,769,728</span> │ concatenate_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2d_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">590,080</span> │ conv2d_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]         │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ up_sampling2d_2           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]         │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">UpSampling2D</span>)            │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ concatenate_2             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">384</span>)    │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ up_sampling2d_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>], │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Concatenate</span>)             │                        │                │ block2_conv2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]     │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2d_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">442,496</span> │ concatenate_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2d_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">147,584</span> │ conv2d_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]         │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ up_sampling2d_3           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)  │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d_5[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]         │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">UpSampling2D</span>)            │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ concatenate_3             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">192</span>)  │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ up_sampling2d_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>], │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Concatenate</span>)             │                        │                │ block1_conv2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]     │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2d_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)   │        <span style=\"color: #00af00; text-decoration-color: #00af00\">110,656</span> │ concatenate_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2d_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)   │         <span style=\"color: #00af00; text-decoration-color: #00af00\">36,928</span> │ conv2d_6[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]         │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2d_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">65</span> │ conv2d_7[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]         │\n",
              "└───────────────────────────┴────────────────────────┴────────────────┴────────────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m24,891,137\u001b[0m (94.95 MB)\n"
            ],
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              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">24,891,137</span> (94.95 MB)\n",
              "</pre>\n"
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m24,891,137\u001b[0m (94.95 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">24,891,137</span> (94.95 MB)\n",
              "</pre>\n"
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          "metadata": {}
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          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
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            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Define U-Net model with pretrained VGG16 encoder\n",
        "def unet_model(input_size=(128, 128, 3)):\n",
        "    inputs = Input(input_size)\n",
        "    vgg16 = VGG16(include_top=False, weights='imagenet', input_tensor=inputs)\n",
        "\n",
        "    # Encoder\n",
        "    conv1 = vgg16.get_layer('block1_conv2').output\n",
        "    pool1 = vgg16.get_layer('block1_pool').output\n",
        "    conv2 = vgg16.get_layer('block2_conv2').output\n",
        "    pool2 = vgg16.get_layer('block2_pool').output\n",
        "    conv3 = vgg16.get_layer('block3_conv3').output\n",
        "    pool3 = vgg16.get_layer('block3_pool').output\n",
        "    conv4 = vgg16.get_layer('block4_conv3').output\n",
        "    pool4 = vgg16.get_layer('block4_pool').output\n",
        "    conv5 = vgg16.get_layer('block5_conv3').output\n",
        "\n",
        "    # Decoder\n",
        "    up6 = concatenate([UpSampling2D(size=(2, 2))(conv5), conv4], axis=-1)\n",
        "    conv6 = Conv2D(512, 3, activation='relu', padding='same')(up6)\n",
        "    conv6 = Conv2D(512, 3, activation='relu', padding='same')(conv6)\n",
        "\n",
        "    up7 = concatenate([UpSampling2D(size=(2, 2))(conv6), conv3], axis=-1)\n",
        "    conv7 = Conv2D(256, 3, activation='relu', padding='same')(up7)\n",
        "    conv7 = Conv2D(256, 3, activation='relu', padding='same')(conv7)\n",
        "\n",
        "    up8 = concatenate([UpSampling2D(size=(2, 2))(conv7), conv2], axis=-1)\n",
        "    conv8 = Conv2D(128, 3, activation='relu', padding='same')(up8)\n",
        "    conv8 = Conv2D(128, 3, activation='relu', padding='same')(conv8)\n",
        "\n",
        "    up9 = concatenate([UpSampling2D(size=(2, 2))(conv8), conv1], axis=-1)\n",
        "    conv9 = Conv2D(64, 3, activation='relu', padding='same')(up9)\n",
        "    conv9 = Conv2D(64, 3, activation='relu', padding='same')(conv9)\n",
        "\n",
        "    outputs = Conv2D(1, 1, activation='sigmoid')(conv9)\n",
        "\n",
        "    model = Model(inputs=[inputs], outputs=[outputs])\n",
        "    model.compile(optimizer=Adam(), loss='binary_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "    return model\n",
        "\n",
        "# Create the model\n",
        "model = unet_model()\n",
        "\n",
        "#evo\n",
        "# Eğer daha önce kaydedilmiş en iyi ağırlıklar varsa yükle\n",
        "if os.path.exists(BEST_WEIGHTS_PATH):\n",
        "    print(\"Loading best weights before training...\")\n",
        "    model.load_weights(BEST_WEIGHTS_PATH)\n",
        "else:\n",
        "    print(\"No best weights found, starting with default weights.\")\n",
        "\n",
        "\n",
        "# Print model summary\n",
        "model.summary()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y_UaIjya8YuE",
        "outputId": "d2b8a336-ccbd-43aa-c75f-1f2a4b5ea7ce"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m61/61\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m87s\u001b[0m 853ms/step - accuracy: 0.5286 - loss: 2.2762 - val_accuracy: 0.4153 - val_loss: 0.7833\n",
            "Epoch 2/50\n",
            "\u001b[1m61/61\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 215ms/step - accuracy: 0.6069 - loss: 0.6592 - val_accuracy: 0.6319 - val_loss: 0.6440\n",
            "Epoch 3/50\n",
            "\u001b[1m61/61\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 216ms/step - accuracy: 0.6337 - loss: 0.6468 - val_accuracy: 0.6362 - val_loss: 0.6839\n",
            "Epoch 4/50\n",
            "\u001b[1m61/61\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 216ms/step - accuracy: 0.6555 - loss: 0.6306 - val_accuracy: 0.6337 - val_loss: 0.6729\n",
            "Epoch 5/50\n",
            "\u001b[1m61/61\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 217ms/step - accuracy: 0.6551 - loss: 0.6333 - val_accuracy: 0.5756 - val_loss: 0.6924\n",
            "Epoch 6/50\n",
            "\u001b[1m61/61\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 222ms/step - accuracy: 0.6523 - loss: 0.6351 - val_accuracy: 0.5852 - val_loss: 0.7512\n",
            "Epoch 7/50\n",
            "\u001b[1m61/61\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 223ms/step - accuracy: 0.6557 - loss: 0.6330 - val_accuracy: 0.6565 - val_loss: 0.6215\n",
            "Epoch 8/50\n",
            "\u001b[1m61/61\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 219ms/step - accuracy: 0.6527 - loss: 0.6297 - val_accuracy: 0.6446 - val_loss: 0.6526\n",
            "Epoch 9/50\n",
            "\u001b[1m61/61\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 220ms/step - accuracy: 0.6595 - loss: 0.6273 - val_accuracy: 0.5962 - val_loss: 0.7031\n",
            "Epoch 10/50\n",
            "\u001b[1m61/61\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 220ms/step - accuracy: 0.6594 - loss: 0.6249 - val_accuracy: 0.6367 - val_loss: 0.6389\n",
            "Epoch 11/50\n",
            "\u001b[1m61/61\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 221ms/step - accuracy: 0.6617 - loss: 0.6210 - val_accuracy: 0.6204 - val_loss: 0.6789\n",
            "Epoch 12/50\n",
            "\u001b[1m61/61\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 222ms/step - accuracy: 0.6640 - loss: 0.6180 - val_accuracy: 0.6604 - val_loss: 0.6138\n",
            "Epoch 13/50\n",
            "\u001b[1m61/61\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 223ms/step - accuracy: 0.6653 - loss: 0.6164 - val_accuracy: 0.6472 - val_loss: 0.6272\n",
            "Epoch 14/50\n",
            "\u001b[1m61/61\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 223ms/step - accuracy: 0.6705 - loss: 0.6108 - val_accuracy: 0.6624 - val_loss: 0.6119\n",
            "Epoch 15/50\n",
            "\u001b[1m61/61\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 227ms/step - accuracy: 0.6655 - loss: 0.6145 - val_accuracy: 0.6867 - val_loss: 0.6063\n",
            "Epoch 16/50\n",
            "\u001b[1m61/61\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 223ms/step - accuracy: 0.6767 - loss: 0.6062 - val_accuracy: 0.6675 - val_loss: 0.6459\n",
            "Epoch 17/50\n",
            "\u001b[1m61/61\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 227ms/step - accuracy: 0.6812 - loss: 0.6053 - val_accuracy: 0.6641 - val_loss: 0.6414\n",
            "Epoch 18/50\n",
            "\u001b[1m61/61\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 222ms/step - accuracy: 0.6982 - loss: 0.5885 - val_accuracy: 0.7379 - val_loss: 0.5320\n",
            "Epoch 19/50\n",
            "\u001b[1m61/61\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 221ms/step - accuracy: 0.6661 - loss: 0.6151 - val_accuracy: 0.7132 - val_loss: 0.5760\n",
            "Epoch 20/50\n",
            "\u001b[1m61/61\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 222ms/step - accuracy: 0.6853 - loss: 0.5986 - val_accuracy: 0.7229 - val_loss: 0.5544\n",
            "Epoch 21/50\n",
            "\u001b[1m61/61\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 222ms/step - accuracy: 0.7039 - loss: 0.5754 - val_accuracy: 0.6772 - val_loss: 0.6034\n",
            "Epoch 22/50\n",
            "\u001b[1m61/61\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 222ms/step - accuracy: 0.6883 - loss: 0.5949 - val_accuracy: 0.7141 - val_loss: 0.5521\n",
            "Epoch 23/50\n",
            "\u001b[1m61/61\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 222ms/step - accuracy: 0.7011 - loss: 0.5769 - val_accuracy: 0.7263 - val_loss: 0.5348\n",
            "Epoch 24/50\n",
            "\u001b[1m61/61\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 222ms/step - accuracy: 0.7130 - loss: 0.5649 - val_accuracy: 0.6662 - val_loss: 0.5960\n",
            "Epoch 25/50\n",
            "\u001b[1m61/61\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 226ms/step - accuracy: 0.7107 - loss: 0.5660 - val_accuracy: 0.7146 - val_loss: 0.5468\n",
            "Epoch 26/50\n",
            "\u001b[1m61/61\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 222ms/step - accuracy: 0.7123 - loss: 0.5625 - val_accuracy: 0.7285 - val_loss: 0.5398\n",
            "Epoch 27/50\n",
            "\u001b[1m61/61\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 221ms/step - accuracy: 0.7069 - loss: 0.5680 - val_accuracy: 0.6611 - val_loss: 0.6301\n",
            "Epoch 28/50\n",
            "\u001b[1m61/61\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 222ms/step - accuracy: 0.7150 - loss: 0.5584 - val_accuracy: 0.7203 - val_loss: 0.5450\n",
            "Epoch 29/50\n",
            "\u001b[1m61/61\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 221ms/step - accuracy: 0.7195 - loss: 0.5498 - val_accuracy: 0.7295 - val_loss: 0.5271\n",
            "Epoch 30/50\n",
            "\u001b[1m61/61\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 221ms/step - accuracy: 0.7103 - loss: 0.5589 - val_accuracy: 0.7006 - val_loss: 0.5691\n",
            "Epoch 31/50\n",
            "\u001b[1m61/61\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 226ms/step - accuracy: 0.7261 - loss: 0.5433 - val_accuracy: 0.7143 - val_loss: 0.5517\n",
            "Epoch 32/50\n",
            "\u001b[1m61/61\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 221ms/step - accuracy: 0.7291 - loss: 0.5418 - val_accuracy: 0.7072 - val_loss: 0.5673\n",
            "Epoch 33/50\n",
            "\u001b[1m61/61\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 220ms/step - accuracy: 0.7278 - loss: 0.5429 - val_accuracy: 0.7111 - val_loss: 0.5499\n",
            "Epoch 34/50\n",
            "\u001b[1m61/61\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 222ms/step - accuracy: 0.7388 - loss: 0.5298 - val_accuracy: 0.7245 - val_loss: 0.5211\n",
            "Epoch 35/50\n",
            "\u001b[1m61/61\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 222ms/step - accuracy: 0.7300 - loss: 0.5356 - val_accuracy: 0.6821 - val_loss: 0.6022\n",
            "Epoch 36/50\n",
            "\u001b[1m61/61\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 223ms/step - accuracy: 0.7321 - loss: 0.5318 - val_accuracy: 0.6750 - val_loss: 0.6017\n",
            "Epoch 37/50\n",
            "\u001b[1m61/61\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 223ms/step - accuracy: 0.7352 - loss: 0.5296 - val_accuracy: 0.6911 - val_loss: 0.5863\n",
            "Epoch 38/50\n",
            "\u001b[1m61/61\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 222ms/step - accuracy: 0.7433 - loss: 0.5151 - val_accuracy: 0.7352 - val_loss: 0.5279\n",
            "Epoch 39/50\n",
            "\u001b[1m61/61\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 221ms/step - accuracy: 0.7507 - loss: 0.5019 - val_accuracy: 0.6686 - val_loss: 0.5780\n",
            "Epoch 40/50\n",
            "\u001b[1m61/61\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 220ms/step - accuracy: 0.7264 - loss: 0.5371 - val_accuracy: 0.7016 - val_loss: 0.6167\n",
            "Epoch 41/50\n",
            "\u001b[1m61/61\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 220ms/step - accuracy: 0.7529 - loss: 0.5075 - val_accuracy: 0.6710 - val_loss: 0.6318\n",
            "Epoch 42/50\n",
            "\u001b[1m61/61\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 221ms/step - accuracy: 0.7456 - loss: 0.5133 - val_accuracy: 0.7305 - val_loss: 0.5184\n",
            "Epoch 43/50\n",
            "\u001b[1m61/61\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 222ms/step - accuracy: 0.7395 - loss: 0.5249 - val_accuracy: 0.6626 - val_loss: 0.5934\n",
            "Epoch 44/50\n",
            "\u001b[1m61/61\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 220ms/step - accuracy: 0.7576 - loss: 0.4967 - val_accuracy: 0.7044 - val_loss: 0.5525\n",
            "Epoch 45/50\n",
            "\u001b[1m61/61\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 220ms/step - accuracy: 0.7595 - loss: 0.4936 - val_accuracy: 0.7436 - val_loss: 0.5248\n",
            "Epoch 46/50\n",
            "\u001b[1m61/61\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 220ms/step - accuracy: 0.7480 - loss: 0.5071 - val_accuracy: 0.7200 - val_loss: 0.5470\n",
            "Epoch 47/50\n",
            "\u001b[1m61/61\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 220ms/step - accuracy: 0.7666 - loss: 0.4847 - val_accuracy: 0.6679 - val_loss: 0.7603\n",
            "Epoch 48/50\n",
            "\u001b[1m61/61\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 220ms/step - accuracy: 0.7652 - loss: 0.4871 - val_accuracy: 0.7384 - val_loss: 0.5168\n",
            "Epoch 49/50\n",
            "\u001b[1m61/61\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 221ms/step - accuracy: 0.7642 - loss: 0.4826 - val_accuracy: 0.7160 - val_loss: 0.5940\n",
            "Epoch 50/50\n",
            "\u001b[1m61/61\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 222ms/step - accuracy: 0.7603 - loss: 0.4855 - val_accuracy: 0.7472 - val_loss: 0.5216\n"
          ]
        }
      ],
      "source": [
        "train_datagen = ImageDataGenerator(rotation_range=20, width_shift_range=0.1, height_shift_range=0.1, horizontal_flip=True)\n",
        "valid_datagen = ImageDataGenerator()\n",
        "\n",
        "train_generator = train_datagen.flow(train_images, train_masks, batch_size=16)\n",
        "valid_generator = valid_datagen.flow(valid_images, valid_masks, batch_size=16)\n",
        "\n",
        "#history = model.fit(train_generator, validation_data=valid_generator, epochs=50) #delete this pls\n",
        "\n",
        "# En iyi ağırlıkları kaydetmek için ModelCheckpoint kullanımı #evo\n",
        "checkpoint = ModelCheckpoint(BEST_WEIGHTS_PATH, monitor='val_loss', save_best_only=True, mode='min', verbose=1)\n",
        "\n",
        "# Modeli eğitimle beraber en iyi ağırlıkları kaydedecek şekilde eğitme\n",
        "history = model.fit(train_generator, validation_data=valid_generator, epochs=50, callbacks=[checkpoint])\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 539
        },
        "id": "DD2nQpyI8Yro",
        "outputId": "a97ca5da-8a75-4af7-c42e-8bc89e224e4b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m26s\u001b[0m 11s/step - accuracy: 0.7747 - loss: 0.4620\n",
            "Test Loss: 0.4680505096912384\n",
            "Test Accuracy: 0.77131187915802\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "loss, accuracy = model.evaluate(test_images, test_masks)\n",
        "print(f'Test Loss: {loss}')\n",
        "print(f'Test Accuracy: {accuracy}')\n",
        "\n",
        "# Create subplots\n",
        "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))\n",
        "\n",
        "# Plot training and validation loss\n",
        "ax1.plot(history.history['loss'], label='train loss')\n",
        "ax1.plot(history.history['val_loss'], label='val loss')\n",
        "ax1.set_xlabel('Epoch')\n",
        "ax1.set_ylabel('Loss')\n",
        "ax1.set_title('Training and Validation Loss')\n",
        "ax1.legend()\n",
        "\n",
        "# Plot training and validation accuracy\n",
        "ax2.plot(history.history['accuracy'], label='train accuracy')\n",
        "ax2.plot(history.history['val_accuracy'], label='val accuracy')\n",
        "ax2.set_xlabel('Epoch')\n",
        "ax2.set_ylabel('Accuracy')\n",
        "ax2.set_title('Training and Validation Accuracy')\n",
        "ax2.legend()\n",
        "\n",
        "# Show the plots\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#model = unet_model()   #evo\n",
        "trainable_params = model.count_params()  # weight counter\n",
        "print(f'Trainable ağırlık sayısı: {trainable_params}')  # print\n",
        "\n",
        "# Eğitim tamamlandıktan sonra modeli değerlendir #evo\n",
        "loss, accuracy = model.evaluate(test_images, test_masks)\n",
        "print(f'Test Loss: {loss}')\n",
        "print(f'Test Accuracy: {accuracy}')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ftuv8ixz1uc7",
        "outputId": "0c066506-6246-4ca0-87b9-20c976c0c325"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Trainable ağırlık sayısı: 24891137\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#save the model to drive\n",
        "\n",
        "from tensorflow.keras.models import Sequential\n",
        "\n",
        "# Örneğin, model oluşturulduktan sonra:\n",
        "model.save('unet_model.h5')  # Modeli Colab ortamına kaydet\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "model.save('/content/drive/My Drive/unet_model.h5')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376
        },
        "id": "8RXMUhfWzklT",
        "outputId": "5d5f33c5-f503-4bc8-c1c5-d02c0b866b6e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
          ]
        },
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "mount failed",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-12-02a0f3a04729>\u001b[0m in \u001b[0;36m<cell line: 9>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mgoogle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolab\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdrive\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mdrive\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/content/drive'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/content/drive/My Drive/unet_model.h5'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/google/colab/drive.py\u001b[0m in \u001b[0;36mmount\u001b[0;34m(mountpoint, force_remount, timeout_ms, readonly)\u001b[0m\n\u001b[1;32m     98\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmountpoint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mforce_remount\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout_ms\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m120000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreadonly\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     99\u001b[0m   \u001b[0;34m\"\"\"Mount your Google Drive at the specified mountpoint path.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 100\u001b[0;31m   return _mount(\n\u001b[0m\u001b[1;32m    101\u001b[0m       \u001b[0mmountpoint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    102\u001b[0m       \u001b[0mforce_remount\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mforce_remount\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/google/colab/drive.py\u001b[0m in \u001b[0;36m_mount\u001b[0;34m(mountpoint, force_remount, timeout_ms, ephemeral, readonly)\u001b[0m\n\u001b[1;32m    285\u001b[0m             \u001b[0;34m'https://research.google.com/colaboratory/faq.html#drive-timeout'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    286\u001b[0m         )\n\u001b[0;32m--> 287\u001b[0;31m       \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'mount failed'\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mextra_reason\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    288\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mcase\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    289\u001b[0m       \u001b[0;31m# Terminate the DriveFS binary before killing bash.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: mount failed"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1FJHwroi8YpQ"
      },
      "outputs": [],
      "source": [
        "import random\n",
        "\n",
        "# Function to display images, masks, and predictions with white masks\n",
        "def display_predictions(model, images, masks, num_examples=15):\n",
        "    indices = random.sample(range(len(images)), num_examples)\n",
        "\n",
        "    plt.figure(figsize=(15, num_examples * 5))\n",
        "    for i, idx in enumerate(indices):\n",
        "        image = images[idx]\n",
        "        mask = masks[idx]\n",
        "        prediction = model.predict(np.expand_dims(image, axis=0))[0]\n",
        "\n",
        "        # Threshold the prediction to create binary mask\n",
        "        thresholded_prediction = (prediction > 0.5).astype(np.uint8) * 255\n",
        "\n",
        "        plt.subplot(num_examples, 3, i * 3 + 1)\n",
        "        plt.title('Original Image')\n",
        "        plt.imshow(image)\n",
        "        plt.axis('off')\n",
        "\n",
        "        plt.subplot(num_examples, 3, i * 3 + 2)\n",
        "        plt.title('Ground Truth Mask')\n",
        "        plt.imshow(mask.squeeze(), cmap='gray')\n",
        "        plt.axis('off')\n",
        "\n",
        "        plt.subplot(num_examples, 3, i * 3 + 3)\n",
        "        plt.title('Predicted Mask')\n",
        "        plt.imshow(thresholded_prediction.squeeze(), cmap='gray')\n",
        "        plt.axis('off')\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "\n",
        "# Display prediction examples on test images\n",
        "display_predictions(model, test_images, test_masks)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "LRm0cC9qywJb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IrD9XWFUGbD0"
      },
      "outputs": [],
      "source": [
        "def display_new_predictions(model, new_images, original_images, num_examples=5):\n",
        "    indices = random.sample(range(len(new_images)), num_examples)\n",
        "\n",
        "    plt.figure(figsize=(15, num_examples * 5))\n",
        "    for i, idx in enumerate(indices):\n",
        "        image = new_images[idx]\n",
        "        original_image = original_images[idx]\n",
        "        prediction = model.predict(np.expand_dims(image, axis=0))[0]\n",
        "\n",
        "        # Threshold the prediction to create binary mask\n",
        "        thresholded_prediction = (prediction > 0.5).astype(np.uint8) * 255\n",
        "\n",
        "        plt.subplot(num_examples, 3, i * 3 + 1)\n",
        "        plt.title('Original Image')\n",
        "        plt.imshow(original_image)\n",
        "        plt.axis('off')\n",
        "\n",
        "        plt.subplot(num_examples, 3, i * 3 + 2)\n",
        "        plt.title('Processed Image')\n",
        "        plt.imshow(image)\n",
        "        plt.axis('off')\n",
        "\n",
        "        plt.subplot(num_examples, 3, i * 3 + 3)\n",
        "        plt.title('Predicted Mask')\n",
        "        plt.imshow(thresholded_prediction.squeeze(), cmap='gray')\n",
        "        plt.axis('off')\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "\n",
        "# Display prediction examples on new images\n",
        "display_new_predictions(model, new_images, original_images)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np #evo\n",
        "import random\n",
        "from copy import deepcopy\n"
      ],
      "metadata": {
        "id": "urmewweX4Ftt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Ağırlıkları mutasyona uğratma fonksiyonu  #evo\n",
        "def mutate_weights(weights, mutation_rate=0.01):\n",
        "    new_weights = deepcopy(weights)\n",
        "    for i in range(len(new_weights)):\n",
        "        if len(new_weights[i].shape) > 1:\n",
        "            mutation_mask = np.random.rand(*new_weights[i].shape) < mutation_rate\n",
        "            mutation_values = np.random.randn(*new_weights[i].shape)\n",
        "            new_weights[i][mutation_mask] += mutation_values[mutation_mask]\n",
        "        else:\n",
        "            new_weights[i] += np.random.randn(*new_weights[i].shape) * mutation_rate\n",
        "    return new_weights\n"
      ],
      "metadata": {
        "id": "1xJI7BU9a7mp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Ağırlıkları optimize etmek için fonksiyon #evo\n",
        "def optimize_weights(model, train_data, train_masks, generations=20, population_size=10, mutation_rate=0.05):\n",
        "    # En iyi ağırlıkları yükle\n",
        "    if os.path.exists(BEST_WEIGHTS_PATH):\n",
        "        print(\"Loading best weights...\")\n",
        "        model.load_weights(BEST_WEIGHTS_PATH)\n",
        "    else:\n",
        "        print(\"No best weights found, using initial model weights.\")\n",
        "\n",
        "    # Başlangıç popülasyonunu oluştur\n",
        "    population = [deepcopy(model) for _ in range(population_size)]\n",
        "\n",
        "    # Her modelin başlangıç fitness puanlarını hesapla\n",
        "    population_fitness = [fitness_function(m, train_data, train_masks) for m in population]\n",
        "\n",
        "    for generation in range(generations):\n",
        "        print(f'Generation {generation+1}/{generations}')\n",
        "        population_fitness = [fitness if np.isfinite(fitness) else 1e10 for fitness in population_fitness]\n",
        "        selection_weights = [1 / fitness for fitness in population_fitness]\n",
        "\n",
        "        if not all(np.isfinite(selection_weights)) or sum(selection_weights) == 0:\n",
        "            selection_weights = [1e-6] * len(selection_weights)\n",
        "\n",
        "        next_population = []\n",
        "        for i in range(population_size):\n",
        "            parent1, parent2 = random.choices(population, weights=selection_weights, k=2)\n",
        "            child = deepcopy(parent1)\n",
        "            child_weights = child.get_weights()\n",
        "            child_weights = mutate_weights(child_weights, mutation_rate)\n",
        "            child.set_weights(child_weights)\n",
        "            next_population.append(child)\n",
        "\n",
        "        population = next_population\n",
        "        population_fitness = [fitness_function(m, train_data, train_masks) for m in population]\n",
        "        best_fitness = min(population_fitness)\n",
        "        print(f'Best Fitness: {best_fitness}')\n",
        "\n",
        "    best_model = population[np.argmin(population_fitness)]\n",
        "    best_model.save('/content/drive/My Drive/optimized_unet_model.h5')  # Optimize edilmiş modeli kaydet\n",
        "    return best_model\n"
      ],
      "metadata": {
        "id": "ZAfqr0m6a94g"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "AWkCQiCLbAnD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#evo\n",
        "# Fitness fonksiyonu (loss'a dayalı)\n",
        "def fitness_function(model, train_data, train_masks):\n",
        "    score = model.evaluate(train_data, train_masks, verbose=0)\n",
        "    return score[0]  # Loss'u kullanabiliriz (düşük olması daha iyi)\n"
      ],
      "metadata": {
        "id": "_Paco5A5bD_U"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Modeli optimize et  #evo\n",
        "optimized_model = optimize_weights(model, train_images, train_masks, generations=3, population_size=2, mutation_rate=0.01) # have to generations=20, population_size=10-20, mutation_rate=0.01-0.05\n",
        "\n",
        "# Test verileri üzerinde optimize edilmiş modeli değerlendir\n",
        "final_loss, final_accuracy = optimized_model.evaluate(test_images, test_masks)\n",
        "print(f'Optimized Test Loss: {final_loss}')\n",
        "print(f'Optimized Test Accuracy: {final_accuracy}')\n"
      ],
      "metadata": {
        "id": "2taSBXRGbG2Y"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}