Bone_Age_UNet.ipynb

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        "<a href=\"https://colab.research.google.com/github/Armin-Abdollahi/Bone-Age-Estimation/blob/main/Bone_Age_UNet.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UNQa0EiNXsFS",
        "outputId": "446e576a-9ef0-48cb-faaf-96c7421d76ce"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "EqFmUn5vXIeR"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "import matplotlib.pyplot as plt\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.models import Sequential, Model\n",
        "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout, Input, UpSampling2D, concatenate\n",
        "from tensorflow.keras.optimizers import Adam"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the labels\n",
        "labels_path = '/content/drive/MyDrive/Digital_Hand_Atlas_Dataset/Total_Images/Bordered_XRay_Images/Label.csv'\n",
        "labels = pd.read_csv(labels_path)"
      ],
      "metadata": {
        "id": "iZpmkwySXyrC"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "labels['Case ID'] = labels['Case ID'].apply(lambda x: str(x)+'.jpg')\n",
        "labels.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ZnI_2HEoX2H_",
        "outputId": "d0ebd5d0-e75e-4b8a-d738-2d313e068e12"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Case ID  Bone Age   Male Location\n",
              "0   1.jpg         0  False    Asian\n",
              "1   2.jpg         0  False    Asian\n",
              "2   3.jpg         0  False    Asian\n",
              "3   4.jpg         0  False    Asian\n",
              "4   5.jpg         0  False    Asian"
            ],
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "labels",
              "summary": "{\n  \"name\": \"labels\",\n  \"rows\": 7393,\n  \"fields\": [\n    {\n      \"column\": \"Case ID\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 7393,\n        \"samples\": [\n          \"7270.jpg\",\n          \"6771.jpg\",\n          \"1540.jpg\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Bone Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5,\n        \"min\": 0,\n        \"max\": 18,\n        \"num_unique_values\": 19,\n        \"samples\": [\n          0,\n          5,\n          11\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Male\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Location\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"African American\",\n          \"Hispanic American\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Ensure the 'Case ID' column is of type string\n",
        "labels['Case ID'] = labels['Case ID'].astype(str)"
      ],
      "metadata": {
        "id": "Meq4E-U0X6X1"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create the image data generator\n",
        "datagen = ImageDataGenerator(rescale=1./255, validation_split=0.2)"
      ],
      "metadata": {
        "id": "-RJCYJKkX82w"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create the train and validation generators\n",
        "train_generator = datagen.flow_from_dataframe(\n",
        "    dataframe=labels,\n",
        "    directory='/content/drive/MyDrive/Digital_Hand_Atlas_Dataset/Total_Images/Bordered_XRay_Images',\n",
        "    x_col='Case ID',\n",
        "    y_col='Bone Age',\n",
        "    target_size=(256, 256),\n",
        "    color_mode='grayscale',\n",
        "    class_mode='raw',\n",
        "    batch_size=32,\n",
        "    subset='training'\n",
        ")\n",
        "\n",
        "validation_generator = datagen.flow_from_dataframe(\n",
        "    dataframe=labels,\n",
        "    directory='/content/drive/MyDrive/Digital_Hand_Atlas_Dataset/Total_Images/Bordered_XRay_Images',\n",
        "    x_col='Case ID',\n",
        "    y_col='Bone Age',\n",
        "    target_size=(256, 256),\n",
        "    color_mode='grayscale',\n",
        "    class_mode='raw',\n",
        "    batch_size=32,\n",
        "    subset='validation'\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pCBnflwyX-sp",
        "outputId": "3ee7fe7a-b9dc-488f-93d7-d33451055f3d"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 5915 validated image filenames.\n",
            "Found 1478 validated image filenames.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define U-Net architecture\n",
        "def unet_model(input_shape):\n",
        "    inputs = Input(shape=input_shape)\n",
        "\n",
        "    # Encoder\n",
        "    conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(inputs)\n",
        "    conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(conv1)\n",
        "    pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)\n",
        "\n",
        "    # Bottleneck\n",
        "    conv2 = Conv2D(64, (3, 3), activation='relu', padding='same')(pool1)\n",
        "    conv2 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv2)\n",
        "\n",
        "    # Decoder\n",
        "    up1 = UpSampling2D(size=(2, 2))(conv2)\n",
        "    merge1 = concatenate([conv1, up1], axis=-1)\n",
        "    conv3 = Conv2D(32, (3, 3), activation='relu', padding='same')(merge1)\n",
        "    conv3 = Conv2D(32, (3, 3), activation='relu', padding='same')(conv3)\n",
        "\n",
        "    # Output layer\n",
        "    output = Conv2D(1, (1, 1), activation='linear')(conv3)\n",
        "\n",
        "    model = Model(inputs=inputs, outputs=output)\n",
        "    return model"
      ],
      "metadata": {
        "id": "FYjuloA0XRO7"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create the U-Net model\n",
        "input_shape = (256, 256, 1)  # Adjust input shape based on your data\n",
        "model = unet_model(input_shape)"
      ],
      "metadata": {
        "id": "9SxV63kCXUAI"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Compile the model\n",
        "model.compile(optimizer=Adam(), loss='mean_squared_error', metrics=['mae', 'accuracy'])"
      ],
      "metadata": {
        "id": "yLyx4Mo1XVlp"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Print model summary\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 529
        },
        "id": "6O3q9zdAXXAM",
        "outputId": "9e75c994-5f27-441a-fcd6-1022a3c343fa"
      },
      "execution_count": 11,
      "outputs": [
        {
          "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;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m1\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;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m32\u001b[0m)   │            \u001b[38;5;34m320\u001b[0m │ input_layer[\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;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m32\u001b[0m)   │          \u001b[38;5;34m9,248\u001b[0m │ conv2d[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]           │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ max_pooling2d             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m32\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;33mMaxPooling2D\u001b[0m)            │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2d_2 (\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;34m18,496\u001b[0m │ max_pooling2d[\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;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m64\u001b[0m)   │         \u001b[38;5;34m36,928\u001b[0m │ conv2d_2[\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;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m64\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 (\u001b[38;5;33mConcatenate\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m96\u001b[0m)   │              \u001b[38;5;34m0\u001b[0m │ conv2d_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],        │\n",
              "│                           │                        │                │ up_sampling2d[\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;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m32\u001b[0m)   │         \u001b[38;5;34m27,680\u001b[0m │ concatenate[\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;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m32\u001b[0m)   │          \u001b[38;5;34m9,248\u001b[0m │ conv2d_4[\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;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m1\u001b[0m)    │             \u001b[38;5;34m33\u001b[0m │ conv2d_5[\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\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</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\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)   │            <span style=\"color: #00af00; text-decoration-color: #00af00\">320</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",
              "│ 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\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)   │          <span style=\"color: #00af00; text-decoration-color: #00af00\">9,248</span> │ conv2d[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]           │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ max_pooling2d             │ (<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\">32</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\">MaxPooling2D</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\">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\">18,496</span> │ max_pooling2d[<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\">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_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             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</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 (<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\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">96</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",
              "│                           │                        │                │ up_sampling2d[<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\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)   │         <span style=\"color: #00af00; text-decoration-color: #00af00\">27,680</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_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\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)   │          <span style=\"color: #00af00; text-decoration-color: #00af00\">9,248</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",
              "│ 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\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">33</span> │ conv2d_5[<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;34m101,953\u001b[0m (398.25 KB)\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\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">101,953</span> (398.25 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m101,953\u001b[0m (398.25 KB)\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\">101,953</span> (398.25 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\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\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the model\n",
        "history = model.fit(\n",
        "    train_generator,\n",
        "    steps_per_epoch=train_generator.samples // 32,\n",
        "    validation_data=validation_generator,\n",
        "    validation_steps=validation_generator.samples // 32,\n",
        "    epochs=10\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wtbt0p1wXYaq",
        "outputId": "f0e25852-6e38-4710-e929-a77bbf5efd45"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/trainers/data_adapters/py_dataset_adapter.py:121: UserWarning: 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",
            "  self._warn_if_super_not_called()\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1837s\u001b[0m 10s/step - accuracy: 0.0519 - loss: 44.6194 - mae: 5.5337 - val_accuracy: 0.0679 - val_loss: 26.4764 - val_mae: 4.3206\n",
            "Epoch 2/10\n",
            "\u001b[1m  1/184\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m57s\u001b[0m 314ms/step - accuracy: 0.0938 - loss: 35.6049 - mae: 5.3264"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/lib/python3.10/contextlib.py:153: UserWarning: Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches. You may need to use the `.repeat()` function when building your dataset.\n",
            "  self.gen.throw(typ, value, traceback)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.0938 - loss: 35.6049 - mae: 5.3264 - val_accuracy: 0.0000e+00 - val_loss: 27.0030 - val_mae: 4.5457\n",
            "Epoch 3/10\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m306s\u001b[0m 2s/step - accuracy: 0.0498 - loss: 33.6331 - mae: 4.9666 - val_accuracy: 0.0679 - val_loss: 30.2878 - val_mae: 4.6792\n",
            "Epoch 4/10\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 171us/step - accuracy: 0.0625 - loss: 36.3949 - mae: 5.4073 - val_accuracy: 0.0000e+00 - val_loss: 29.4173 - val_mae: 4.8337\n",
            "Epoch 5/10\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m289s\u001b[0m 2s/step - accuracy: 0.0555 - loss: 31.0698 - mae: 4.8290 - val_accuracy: 0.0679 - val_loss: 27.3215 - val_mae: 4.4178\n",
            "Epoch 6/10\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 129ms/step - accuracy: 0.0625 - loss: 29.9129 - mae: 4.9265 - val_accuracy: 0.0000e+00 - val_loss: 26.0215 - val_mae: 4.5000\n",
            "Epoch 7/10\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m301s\u001b[0m 2s/step - accuracy: 0.0515 - loss: 31.2139 - mae: 4.7952 - val_accuracy: 0.0679 - val_loss: 29.3012 - val_mae: 4.5979\n",
            "Epoch 8/10\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 162us/step - accuracy: 0.0625 - loss: 36.3957 - mae: 5.4489 - val_accuracy: 0.0000e+00 - val_loss: 26.2417 - val_mae: 4.0189\n",
            "Epoch 9/10\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m325s\u001b[0m 2s/step - accuracy: 0.0500 - loss: 31.0922 - mae: 4.8298 - val_accuracy: 0.0673 - val_loss: 28.5503 - val_mae: 4.5236\n",
            "Epoch 10/10\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145us/step - accuracy: 0.0312 - loss: 32.7784 - mae: 4.9954 - val_accuracy: 0.1667 - val_loss: 49.5378 - val_mae: 6.7048\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot training & validation MAE values\n",
        "plt.figure(figsize=(18, 4))\n",
        "\n",
        "plt.subplot(1, 3, 1)\n",
        "plt.plot(history.history['mae'])\n",
        "plt.plot(history.history['val_mae'])\n",
        "plt.title('Model MAE')\n",
        "plt.ylabel('MAE')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Train', 'Validation'], loc='upper left')\n",
        "\n",
        "# Plot training & validation loss values\n",
        "plt.subplot(1, 3, 2)\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('Model Loss')\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Train', 'Validation'], loc='upper left')\n",
        "\n",
        "# Plot training & validation accuracy values\n",
        "plt.subplot(1, 3, 3)\n",
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.title('Model Accuracy')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Train', 'Validation'], loc='upper left')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "2-BoAkJmXY2I",
        "outputId": "ba5e6baa-33e8-4ce1-d0cb-04ad92d45773"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x400 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the model\n",
        "history = model.fit(\n",
        "    train_generator,\n",
        "    steps_per_epoch=train_generator.samples // 32,\n",
        "    validation_data=validation_generator,\n",
        "    validation_steps=validation_generator.samples // 32,\n",
        "    epochs=15\n",
        ")"
      ],
      "metadata": {
        "id": "X2am7zNLXcNB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot training & validation MAE values\n",
        "plt.figure(figsize=(18, 4))\n",
        "\n",
        "plt.subplot(1, 3, 1)\n",
        "plt.plot(history.history['mae'])\n",
        "plt.plot(history.history['val_mae'])\n",
        "plt.title('Model MAE')\n",
        "plt.ylabel('MAE')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Train', 'Validation'], loc='upper left')\n",
        "\n",
        "# Plot training & validation loss values\n",
        "plt.subplot(1, 3, 2)\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('Model Loss')\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Train', 'Validation'], loc='upper left')\n",
        "\n",
        "# Plot training & validation accuracy values\n",
        "plt.subplot(1, 3, 3)\n",
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.title('Model Accuracy')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Train', 'Validation'], loc='upper left')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "XyzfRWB7XeqM"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}