Bone_Age_VGG19.ipynb

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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "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.applications import VGG19\n",
        "from tensorflow.keras.layers import Flatten, Dense, Dropout\n",
        "from tensorflow.keras.optimizers import Adam"
      ],
      "metadata": {
        "id": "rNVsSfzHmWRn"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "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": "0tEXcZGqmxoc"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "labels['Case ID'] = labels['Case ID'].apply(lambda x: str(x)+'.jpg')\n",
        "\n",
        "labels.head()"
      ],
      "metadata": {
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          "height": 206
        },
        "id": "wF6KUwxhmzdn",
        "outputId": "d3fbbcc7-ade0-4c0e-b59a-11f924ec1797"
      },
      "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": "zTSASmhLm1fx"
      },
      "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": "bZ1EyVxSm3aQ"
      },
      "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='rgb',  # Change to 'rgb' for 3 channels\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='rgb',  # Change to 'rgb' for 3 channels\n",
        "    class_mode='raw',\n",
        "    batch_size=32,\n",
        "    subset='validation'\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3eL6PRZCm5V4",
        "outputId": "1a6b046a-767f-4d23-a8c4-654c4ef4dc31"
      },
      "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": [
        "# Build the VGG19 model\n",
        "base_model = VGG19(include_top=False, input_shape=(256, 256, 3))  # Adjust input shape\n",
        "model = tf.keras.Sequential([\n",
        "    base_model,\n",
        "    Flatten(),\n",
        "    Dense(512, activation='relu'),\n",
        "    Dropout(0.5),\n",
        "    Dense(1, activation='linear')  # Linear activation for regression\n",
        "])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w9M1eiJCm7Ui",
        "outputId": "ba762c49-445d-44d1-dcc1-b811ff1fc4d2"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg19/vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "\u001b[1m80134624/80134624\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Compile the model\n",
        "model.compile(optimizer=Adam(), loss='mean_squared_error', metrics=['mae', 'accuracy'])"
      ],
      "metadata": {
        "id": "OPFG9bvcm9NM"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "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": "IouItio2nKO0",
        "outputId": "85219e53-4f8f-496e-b787-65a5b3d7e7c1"
      },
      "execution_count": null,
      "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[1m4005s\u001b[0m 21s/step - accuracy: 0.0508 - loss: 493.4228 - mae: 10.5698 - val_accuracy: 0.0679 - val_loss: 16.4625 - val_mae: 3.5386\n",
            "Epoch 2/10\n",
            "\u001b[1m  1/184\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1:59\u001b[0m 654ms/step - accuracy: 0.0312 - loss: 22.3594 - mae: 4.0857"
          ]
        },
        {
          "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\b\r\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 57ms/step - accuracy: 0.0312 - loss: 22.3594 - mae: 4.0857 - val_accuracy: 0.0000e+00 - val_loss: 16.4962 - val_mae: 3.5798\n",
            "Epoch 3/10\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m329s\u001b[0m 2s/step - accuracy: 0.0566 - loss: 26.1012 - mae: 4.1057 - val_accuracy: 0.0673 - val_loss: 6.8524 - val_mae: 2.1324\n",
            "Epoch 4/10\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 130ms/step - accuracy: 0.0625 - loss: 9.7810 - mae: 2.3450 - val_accuracy: 0.1667 - val_loss: 4.8298 - val_mae: 1.8662\n",
            "Epoch 5/10\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m355s\u001b[0m 2s/step - accuracy: 0.0536 - loss: 10.2334 - mae: 2.4302 - val_accuracy: 0.0795 - val_loss: 4.6115 - val_mae: 1.6555\n",
            "Epoch 6/10\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 491us/step - accuracy: 0.0312 - loss: 6.7629 - mae: 2.1765 - val_accuracy: 0.0000e+00 - val_loss: 7.4081 - val_mae: 2.1508\n",
            "Epoch 7/10\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m332s\u001b[0m 2s/step - accuracy: 0.0560 - loss: 8.5989 - mae: 2.2254 - val_accuracy: 0.0679 - val_loss: 4.6994 - val_mae: 1.7508\n",
            "Epoch 8/10\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 468us/step - accuracy: 0.0625 - loss: 9.6537 - mae: 2.1604 - val_accuracy: 0.0000e+00 - val_loss: 7.1906 - val_mae: 2.1695\n",
            "Epoch 9/10\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m328s\u001b[0m 2s/step - accuracy: 0.0528 - loss: 7.7039 - mae: 2.1025 - val_accuracy: 0.0673 - val_loss: 4.5046 - val_mae: 1.7284\n",
            "Epoch 10/10\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 482us/step - accuracy: 0.0312 - loss: 6.9276 - mae: 2.1074 - val_accuracy: 0.1667 - val_loss: 5.0907 - val_mae: 1.9564\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": {
        "id": "YC1Ai_mBnNht",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        },
        "outputId": "a828d4aa-31e0-4f3e-888f-32982296f620"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x400 with 3 Axes>"
            ],
            "image/png": 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          },
          "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=18\n",
        ")"
      ],
      "metadata": {
        "id": "VQ6fyIADnRQF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d61badcb-3390-44dd-8e8a-26ca94c0fc74"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/18\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[1m2417s\u001b[0m 12s/step - accuracy: 0.0554 - loss: 517952.4062 - mae: 147.2007 - val_accuracy: 0.0679 - val_loss: 27.6004 - val_mae: 4.4579\n",
            "Epoch 2/18\n",
            "\u001b[1m  1/184\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1:58\u001b[0m 647ms/step - accuracy: 0.0625 - loss: 31.9130 - mae: 4.9360"
          ]
        },
        {
          "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\b\r\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 48ms/step - accuracy: 0.0625 - loss: 31.9130 - mae: 4.9360 - val_accuracy: 0.0000e+00 - val_loss: 18.1191 - val_mae: 3.8833\n",
            "Epoch 3/18\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m254s\u001b[0m 1s/step - accuracy: 0.0537 - loss: 24.5726 - mae: 4.1119 - val_accuracy: 0.0700 - val_loss: 6.9486 - val_mae: 2.0566\n",
            "Epoch 4/18\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 375us/step - accuracy: 0.0000e+00 - loss: 7.6630 - mae: 2.1361 - val_accuracy: 0.0000e+00 - val_loss: 0.4644 - val_mae: 0.5346\n",
            "Epoch 5/18\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m268s\u001b[0m 1s/step - accuracy: 0.0498 - loss: 9.6332 - mae: 2.3738 - val_accuracy: 0.0693 - val_loss: 8.2907 - val_mae: 2.3513\n",
            "Epoch 6/18\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 417us/step - accuracy: 0.0000e+00 - loss: 6.0567 - mae: 1.9501 - val_accuracy: 0.1667 - val_loss: 7.9902 - val_mae: 1.9349\n",
            "Epoch 7/18\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m255s\u001b[0m 1s/step - accuracy: 0.0496 - loss: 8.4597 - mae: 2.2121 - val_accuracy: 0.0720 - val_loss: 4.7197 - val_mae: 1.6437\n",
            "Epoch 8/18\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m36s\u001b[0m 194ms/step - accuracy: 0.0312 - loss: 6.7284 - mae: 2.2345 - val_accuracy: 0.1667 - val_loss: 5.5435 - val_mae: 1.5085\n",
            "Epoch 9/18\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m290s\u001b[0m 1s/step - accuracy: 0.0555 - loss: 7.3491 - mae: 2.0551 - val_accuracy: 0.0679 - val_loss: 4.3916 - val_mae: 1.6500\n",
            "Epoch 10/18\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 360us/step - accuracy: 0.0312 - loss: 7.7026 - mae: 2.0049 - val_accuracy: 0.0000e+00 - val_loss: 15.2710 - val_mae: 3.2494\n",
            "Epoch 11/18\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m258s\u001b[0m 1s/step - accuracy: 0.0578 - loss: 6.8668 - mae: 1.9794 - val_accuracy: 0.0679 - val_loss: 4.3378 - val_mae: 1.5690\n",
            "Epoch 12/18\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 394us/step - accuracy: 0.0625 - loss: 7.7474 - mae: 1.9621 - val_accuracy: 0.0000e+00 - val_loss: 10.3567 - val_mae: 2.6586\n",
            "Epoch 13/18\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m256s\u001b[0m 1s/step - accuracy: 0.0498 - loss: 6.4871 - mae: 1.9430 - val_accuracy: 0.0679 - val_loss: 5.3953 - val_mae: 1.9229\n",
            "Epoch 14/18\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 436us/step - accuracy: 0.0938 - loss: 9.6316 - mae: 1.9761 - val_accuracy: 0.1667 - val_loss: 3.8448 - val_mae: 1.5183\n",
            "Epoch 15/18\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m262s\u001b[0m 1s/step - accuracy: 0.0552 - loss: 6.4144 - mae: 1.9182 - val_accuracy: 0.0679 - val_loss: 5.0176 - val_mae: 1.8006\n",
            "Epoch 16/18\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 452us/step - accuracy: 0.0625 - loss: 6.8693 - mae: 1.9323 - val_accuracy: 0.0000e+00 - val_loss: 6.3568 - val_mae: 2.4099\n",
            "Epoch 17/18\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m252s\u001b[0m 1s/step - accuracy: 0.0580 - loss: 6.2297 - mae: 1.8947 - val_accuracy: 0.0679 - val_loss: 3.8559 - val_mae: 1.4836\n",
            "Epoch 18/18\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 381us/step - accuracy: 0.0312 - loss: 8.0600 - mae: 2.0920 - val_accuracy: 0.0000e+00 - val_loss: 4.8640 - val_mae: 1.7967\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": {
        "id": "WK-1fUHenTUg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        },
        "outputId": "16ece40e-962f-4326-ee0a-d19ff14fe37c"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x400 with 3 Axes>"
            ],
            "image/png": 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}