final_stress_predictor_model.ipynb

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        "<a href=\"https://colab.research.google.com/github/VSUrhuel/vsu-stress-predictor/blob/master/final_stress_predictor_model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "execution_count": 228,
      "metadata": {
        "id": "dcnJXZ6mlGEe"
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      "outputs": [],
      "source": [
        "%%capture\n",
        "!pip install -U scikit-learn\n",
        "!pip install -U ppscore\n",
        "!pip install -q ptitprince"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ******************* Data manipulation *******************\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "# ******************* Google Drive *******************\n",
        "from google.colab import drive\n",
        "\n",
        "# ******************* Tenserflow *************************\n",
        "from tensorflow.keras.models import Sequential, load_model\n",
        "from tensorflow.keras.layers import Dense, Input\n",
        "from keras.callbacks import ModelCheckpoint, EarlyStopping\n",
        "\n",
        "# ******************* Data preprocessing ********************\n",
        "from sklearn.model_selection import train_test_split, StratifiedKFold\n",
        "\n",
        "# ******************* Metric ***********************\n",
        "from sklearn.metrics import balanced_accuracy_score, confusion_matrix, accuracy_score\n",
        "from mlxtend.plotting import plot_confusion_matrix\n",
        "\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "Y3enGhMDl4cz"
      },
      "execution_count": 229,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "drive.mount('/content/drive')\n",
        "%cd /content/drive/My Drive/kaggle/input\n",
        "\n",
        "data = pd.read_csv('student_lifestyle_dataset.csv')\n",
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "rp95IGgUmBmZ",
        "outputId": "dd20849d-fb7e-4de0-8138-014f848f7718"
      },
      "execution_count": 230,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n",
            "/content/drive/My Drive/kaggle/input\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Student_ID  Study_Hours_Per_Day  Extracurricular_Hours_Per_Day  \\\n",
              "0           1                  6.9                            3.8   \n",
              "1           2                  5.3                            3.5   \n",
              "2           3                  5.1                            3.9   \n",
              "3           4                  6.5                            2.1   \n",
              "4           5                  8.1                            0.6   \n",
              "\n",
              "   Sleep_Hours_Per_Day  Social_Hours_Per_Day  Physical_Activity_Hours_Per_Day  \\\n",
              "0                  8.7                   2.8                              1.8   \n",
              "1                  8.0                   4.2                              3.0   \n",
              "2                  9.2                   1.2                              4.6   \n",
              "3                  7.2                   1.7                              6.5   \n",
              "4                  6.5                   2.2                              6.6   \n",
              "\n",
              "    GPA Stress_Level  \n",
              "0  2.99     Moderate  \n",
              "1  2.75          Low  \n",
              "2  2.67          Low  \n",
              "3  2.88     Moderate  \n",
              "4  3.51         High  "
            ],
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              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 2000,\n  \"fields\": [\n    {\n      \"column\": \"Student_ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 577,\n        \"min\": 1,\n        \"max\": 2000,\n        \"num_unique_values\": 2000,\n        \"samples\": [\n          1861,\n          354,\n          1334\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Study_Hours_Per_Day\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.423888369586789,\n        \"min\": 5.0,\n        \"max\": 10.0,\n        \"num_unique_values\": 51,\n        \"samples\": [\n          10.0,\n          8.5,\n          7.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Extracurricular_Hours_Per_Day\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1558546578160225,\n        \"min\": 0.0,\n        \"max\": 4.0,\n        \"num_unique_values\": 41,\n        \"samples\": [\n          1.5,\n          1.7,\n          1.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sleep_Hours_Per_Day\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.4609485353055407,\n        \"min\": 5.0,\n        \"max\": 10.0,\n        \"num_unique_values\": 51,\n        \"samples\": [\n          9.9,\n          10.0,\n          5.9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Social_Hours_Per_Day\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.6885140915665202,\n        \"min\": 0.0,\n        \"max\": 6.0,\n        \"num_unique_values\": 61,\n        \"samples\": [\n          2.8,\n          0.3,\n          5.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Physical_Activity_Hours_Per_Day\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.5141100780877554,\n        \"min\": 0.0,\n        \"max\": 13.0,\n        \"num_unique_values\": 118,\n        \"samples\": [\n          0.3,\n          1.4,\n          6.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GPA\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2986735363916102,\n        \"min\": 2.24,\n        \"max\": 4.0,\n        \"num_unique_values\": 158,\n        \"samples\": [\n          2.74,\n          4.0,\n          3.79\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Stress_Level\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Moderate\",\n          \"Low\",\n          \"High\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 230
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def map_stress_level(level):\n",
        "    if level == 'Low':\n",
        "        return 0\n",
        "    elif level == 'Moderate':\n",
        "        return 1\n",
        "    elif level == 'High':\n",
        "        return 2"
      ],
      "metadata": {
        "id": "V23nsuO6mcb8"
      },
      "execution_count": 231,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = pd.get_dummies(data.drop(['Student_ID', 'Stress_Level'], axis=1))\n",
        "y = data['Stress_Level'].apply(map_stress_level)"
      ],
      "metadata": {
        "id": "K2_QzRsKnDnj"
      },
      "execution_count": 232,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=.2)\n",
        "X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=.5)"
      ],
      "metadata": {
        "id": "Ba2JRVqwnLGX"
      },
      "execution_count": 233,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "model.add(Input(shape=(len(X_train.columns),)))\n",
        "model.add(Dense(units=64, activation='relu'))\n",
        "model.add(Dense(units=32, activation='relu'))\n",
        "model.add(Dense(units=3, activation='softmax'))\n",
        "model.compile(loss='sparse_categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "Wr7QRZNSpbZ4"
      },
      "execution_count": 234,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "checkpoint = ModelCheckpoint(\n",
        "    filepath='/content/drive/My Drive/Colab Notebooks/bestModel.keras',\n",
        "    monitor='val_loss',\n",
        "    save_best_only=True,\n",
        "    verbose=1\n",
        ")\n",
        "\n",
        "early_stopping = EarlyStopping(\n",
        "    monitor='val_loss',\n",
        "    patience=30,  # Stop if no improvement after 10 epochs\n",
        "    verbose=1\n",
        ")"
      ],
      "metadata": {
        "id": "U-1a8lcFrEaP"
      },
      "execution_count": 235,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "learn = model.fit(\n",
        "    X_train,\n",
        "    y_train,\n",
        "    validation_data=(X_val, y_val),  # Provide validation data\n",
        "    epochs=100,\n",
        "    batch_size=32,\n",
        "    callbacks=[checkpoint]\n",
        ")\n",
        "print(\"Best validation loss:\", min(learn.history['val_loss']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1q5Fb_n7roG9",
        "outputId": "51150371-e787-4a15-e030-9f80a36650cc"
      },
      "execution_count": 236,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "\u001b[1m39/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.5646 - loss: 0.9660\n",
            "Epoch 1: val_loss improved from inf to 0.64711, saving model to /content/drive/My Drive/Colab Notebooks/bestModel.keras\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - accuracy: 0.5809 - loss: 0.9299 - val_accuracy: 0.7750 - val_loss: 0.6471\n",
            "Epoch 2/100\n",
            "\u001b[1m39/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.7463 - loss: 0.6589\n",
            "Epoch 2: val_loss improved from 0.64711 to 0.55456, saving model to /content/drive/My Drive/Colab Notebooks/bestModel.keras\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.7465 - loss: 0.6543 - val_accuracy: 0.8250 - val_loss: 0.5546\n",
            "Epoch 3/100\n",
            "\u001b[1m35/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7661 - loss: 0.5745\n",
            "Epoch 3: val_loss improved from 0.55456 to 0.49228, saving model to /content/drive/My Drive/Colab Notebooks/bestModel.keras\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.7712 - loss: 0.5688 - val_accuracy: 0.8100 - val_loss: 0.4923\n",
            "Epoch 4/100\n",
            "\u001b[1m32/50\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8322 - loss: 0.4911\n",
            "Epoch 4: val_loss improved from 0.49228 to 0.43312, saving model to /content/drive/My Drive/Colab Notebooks/bestModel.keras\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.8320 - loss: 0.4892 - val_accuracy: 0.8200 - val_loss: 0.4331\n",
            "Epoch 5/100\n",
            "\u001b[1m42/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8410 - loss: 0.4355\n",
            "Epoch 5: val_loss improved from 0.43312 to 0.40041, saving model to /content/drive/My Drive/Colab Notebooks/bestModel.keras\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.8430 - loss: 0.4351 - val_accuracy: 0.8750 - val_loss: 0.4004\n",
            "Epoch 6/100\n",
            "\u001b[1m26/50\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8768 - loss: 0.4049\n",
            "Epoch 6: val_loss improved from 0.40041 to 0.35388, saving model to /content/drive/My Drive/Colab Notebooks/bestModel.keras\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8757 - loss: 0.4039 - val_accuracy: 0.8950 - val_loss: 0.3539\n",
            "Epoch 7/100\n",
            "\u001b[1m43/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8717 - loss: 0.3962\n",
            "Epoch 7: val_loss improved from 0.35388 to 0.32648, saving model to /content/drive/My Drive/Colab Notebooks/bestModel.keras\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8738 - loss: 0.3916 - val_accuracy: 0.8900 - val_loss: 0.3265\n",
            "Epoch 8/100\n",
            "\u001b[1m43/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9023 - loss: 0.3389\n",
            "Epoch 8: val_loss improved from 0.32648 to 0.29815, saving model to /content/drive/My Drive/Colab Notebooks/bestModel.keras\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9006 - loss: 0.3386 - val_accuracy: 0.9000 - val_loss: 0.2981\n",
            "Epoch 9/100\n",
            "\u001b[1m49/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8904 - loss: 0.3221\n",
            "Epoch 9: val_loss improved from 0.29815 to 0.27230, saving model to /content/drive/My Drive/Colab Notebooks/bestModel.keras\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8908 - loss: 0.3217 - val_accuracy: 0.9350 - val_loss: 0.2723\n",
            "Epoch 10/100\n",
            "\u001b[1m44/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9116 - loss: 0.2924\n",
            "Epoch 10: val_loss improved from 0.27230 to 0.26404, saving model to /content/drive/My Drive/Colab Notebooks/bestModel.keras\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9110 - loss: 0.2919 - val_accuracy: 0.9350 - val_loss: 0.2640\n",
            "Epoch 11/100\n",
            "\u001b[1m33/50\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9249 - loss: 0.2792\n",
            "Epoch 11: val_loss improved from 0.26404 to 0.23688, saving model to /content/drive/My Drive/Colab Notebooks/bestModel.keras\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.9222 - loss: 0.2778 - val_accuracy: 0.9550 - val_loss: 0.2369\n",
            "Epoch 12/100\n",
            "\u001b[1m46/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9186 - loss: 0.2559\n",
            "Epoch 12: val_loss improved from 0.23688 to 0.22501, saving model to /content/drive/My Drive/Colab Notebooks/bestModel.keras\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - accuracy: 0.9197 - loss: 0.2550 - val_accuracy: 0.9400 - val_loss: 0.2250\n",
            "Epoch 13/100\n",
            "\u001b[1m49/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9344 - loss: 0.2371\n",
            "Epoch 13: val_loss improved from 0.22501 to 0.21120, saving model to /content/drive/My Drive/Colab Notebooks/bestModel.keras\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9345 - loss: 0.2370 - val_accuracy: 0.9400 - val_loss: 0.2112\n",
            "Epoch 14/100\n",
            "\u001b[1m47/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9300 - loss: 0.2338\n",
            "Epoch 14: val_loss improved from 0.21120 to 0.20471, saving model to /content/drive/My Drive/Colab Notebooks/bestModel.keras\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.9303 - loss: 0.2333 - val_accuracy: 0.9450 - val_loss: 0.2047\n",
            "Epoch 15/100\n",
            "\u001b[1m48/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9313 - loss: 0.2148\n",
            "Epoch 15: val_loss improved from 0.20471 to 0.19372, saving model to /content/drive/My Drive/Colab Notebooks/bestModel.keras\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9313 - loss: 0.2150 - val_accuracy: 0.9500 - val_loss: 0.1937\n",
            "Epoch 16/100\n",
            "\u001b[1m44/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9535 - loss: 0.1958\n",
            "Epoch 16: val_loss improved from 0.19372 to 0.18546, saving model to /content/drive/My Drive/Colab Notebooks/bestModel.keras\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.9533 - loss: 0.1961 - val_accuracy: 0.9500 - val_loss: 0.1855\n",
            "Epoch 17/100\n",
            "\u001b[1m40/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9337 - loss: 0.2069\n",
            "Epoch 17: val_loss improved from 0.18546 to 0.17067, saving model to /content/drive/My Drive/Colab Notebooks/bestModel.keras\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.9354 - loss: 0.2049 - val_accuracy: 0.9600 - val_loss: 0.1707\n",
            "Epoch 18/100\n",
            "\u001b[1m37/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9558 - loss: 0.1718\n",
            "Epoch 18: val_loss did not improve from 0.17067\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9541 - loss: 0.1752 - val_accuracy: 0.9300 - val_loss: 0.1959\n",
            "Epoch 19/100\n",
            "\u001b[1m42/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9584 - loss: 0.1736\n",
            "Epoch 19: val_loss improved from 0.17067 to 0.17047, saving model to /content/drive/My Drive/Colab Notebooks/bestModel.keras\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.9570 - loss: 0.1751 - val_accuracy: 0.9700 - val_loss: 0.1705\n",
            "Epoch 20/100\n",
            "\u001b[1m32/50\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9525 - loss: 0.1717\n",
            "Epoch 20: val_loss did not improve from 0.17047\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.9506 - loss: 0.1719 - val_accuracy: 0.9450 - val_loss: 0.1746\n",
            "Epoch 21/100\n",
            "\u001b[1m34/50\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.9587 - loss: 0.1699\n",
            "Epoch 21: val_loss improved from 0.17047 to 0.15652, saving model to /content/drive/My Drive/Colab Notebooks/bestModel.keras\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9590 - loss: 0.1679 - val_accuracy: 0.9450 - val_loss: 0.1565\n",
            "Epoch 22/100\n",
            "\u001b[1m45/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9496 - loss: 0.1742\n",
            "Epoch 22: val_loss improved from 0.15652 to 0.15635, saving model to /content/drive/My Drive/Colab Notebooks/bestModel.keras\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - accuracy: 0.9499 - loss: 0.1733 - val_accuracy: 0.9650 - val_loss: 0.1564\n",
            "Epoch 23/100\n",
            "\u001b[1m41/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9576 - loss: 0.1606\n",
            "Epoch 23: val_loss improved from 0.15635 to 0.14251, saving model to /content/drive/My Drive/Colab Notebooks/bestModel.keras\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.9572 - loss: 0.1610 - val_accuracy: 0.9650 - val_loss: 0.1425\n",
            "Epoch 24/100\n",
            "\u001b[1m43/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9549 - loss: 0.1584\n",
            "Epoch 24: val_loss improved from 0.14251 to 0.13697, saving model to /content/drive/My Drive/Colab Notebooks/bestModel.keras\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9545 - loss: 0.1580 - val_accuracy: 0.9600 - val_loss: 0.1370\n",
            "Epoch 25/100\n",
            "\u001b[1m45/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9564 - loss: 0.1512\n",
            "Epoch 25: val_loss improved from 0.13697 to 0.12993, saving model to /content/drive/My Drive/Colab Notebooks/bestModel.keras\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - accuracy: 0.9564 - loss: 0.1507 - val_accuracy: 0.9800 - val_loss: 0.1299\n",
            "Epoch 26/100\n",
            "\u001b[1m43/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9665 - loss: 0.1323\n",
            "Epoch 26: val_loss improved from 0.12993 to 0.12889, saving model to /content/drive/My Drive/Colab Notebooks/bestModel.keras\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.9659 - loss: 0.1341 - val_accuracy: 0.9750 - val_loss: 0.1289\n",
            "Epoch 27/100\n",
            "\u001b[1m46/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9527 - loss: 0.1559\n",
            "Epoch 27: val_loss did not improve from 0.12889\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9537 - loss: 0.1546 - val_accuracy: 0.9550 - val_loss: 0.1313\n",
            "Epoch 28/100\n",
            "\u001b[1m27/50\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9404 - loss: 0.1601  \n",
            "Epoch 28: val_loss improved from 0.12889 to 0.12460, saving model to /content/drive/My Drive/Colab Notebooks/bestModel.keras\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9447 - loss: 0.1534 - val_accuracy: 0.9650 - val_loss: 0.1246\n",
            "Epoch 29/100\n",
            "\u001b[1m37/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9449 - loss: 0.1506  \n",
            "Epoch 29: val_loss improved from 0.12460 to 0.11766, saving model to /content/drive/My Drive/Colab Notebooks/bestModel.keras\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9473 - loss: 0.1478 - val_accuracy: 0.9900 - val_loss: 0.1177\n",
            "Epoch 30/100\n",
            "\u001b[1m38/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9717 - loss: 0.1212  \n",
            "Epoch 30: val_loss did not improve from 0.11766\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9696 - loss: 0.1233 - val_accuracy: 0.9350 - val_loss: 0.1511\n",
            "Epoch 31/100\n",
            "\u001b[1m35/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9622 - loss: 0.1284 \n",
            "Epoch 31: val_loss improved from 0.11766 to 0.11023, saving model to /content/drive/My Drive/Colab Notebooks/bestModel.keras\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9621 - loss: 0.1295 - val_accuracy: 0.9950 - val_loss: 0.1102\n",
            "Epoch 32/100\n",
            "\u001b[1m32/50\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9595 - loss: 0.1424  \n",
            "Epoch 32: val_loss did not improve from 0.11023\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9614 - loss: 0.1382 - val_accuracy: 0.9700 - val_loss: 0.1132\n",
            "Epoch 33/100\n",
            "\u001b[1m42/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9660 - loss: 0.1223  \n",
            "Epoch 33: val_loss did not improve from 0.11023\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9643 - loss: 0.1236 - val_accuracy: 0.9750 - val_loss: 0.1124\n",
            "Epoch 34/100\n",
            "\u001b[1m40/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9728 - loss: 0.1150  \n",
            "Epoch 34: val_loss improved from 0.11023 to 0.10380, saving model to /content/drive/My Drive/Colab Notebooks/bestModel.keras\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9706 - loss: 0.1173 - val_accuracy: 0.9700 - val_loss: 0.1038\n",
            "Epoch 35/100\n",
            "\u001b[1m28/50\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9561 - loss: 0.1357  \n",
            "Epoch 35: val_loss improved from 0.10380 to 0.09873, saving model to /content/drive/My Drive/Colab Notebooks/bestModel.keras\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9569 - loss: 0.1326 - val_accuracy: 0.9850 - val_loss: 0.0987\n",
            "Epoch 36/100\n",
            "\u001b[1m28/50\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9739 - loss: 0.1098  \n",
            "Epoch 36: val_loss did not improve from 0.09873\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9705 - loss: 0.1128 - val_accuracy: 0.9350 - val_loss: 0.1508\n",
            "Epoch 37/100\n",
            "\u001b[1m36/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9588 - loss: 0.1288  \n",
            "Epoch 37: val_loss did not improve from 0.09873\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9622 - loss: 0.1257 - val_accuracy: 0.9750 - val_loss: 0.0995\n",
            "Epoch 38/100\n",
            "\u001b[1m40/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9693 - loss: 0.1160  \n",
            "Epoch 38: val_loss improved from 0.09873 to 0.09323, saving model to /content/drive/My Drive/Colab Notebooks/bestModel.keras\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9688 - loss: 0.1163 - val_accuracy: 0.9950 - val_loss: 0.0932\n",
            "Epoch 39/100\n",
            "\u001b[1m34/50\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9745 - loss: 0.1135  \n",
            "Epoch 39: val_loss improved from 0.09323 to 0.09270, saving model to /content/drive/My Drive/Colab Notebooks/bestModel.keras\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9712 - loss: 0.1139 - val_accuracy: 0.9950 - val_loss: 0.0927\n",
            "Epoch 40/100\n",
            "\u001b[1m41/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9716 - loss: 0.1031  \n",
            "Epoch 40: val_loss did not improve from 0.09270\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9704 - loss: 0.1051 - val_accuracy: 0.9450 - val_loss: 0.1238\n",
            "Epoch 41/100\n",
            "\u001b[1m35/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9624 - loss: 0.1122 \n",
            "Epoch 41: val_loss improved from 0.09270 to 0.08867, saving model to /content/drive/My Drive/Colab Notebooks/bestModel.keras\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9638 - loss: 0.1122 - val_accuracy: 0.9700 - val_loss: 0.0887\n",
            "Epoch 42/100\n",
            "\u001b[1m31/50\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9709 - loss: 0.0997  \n",
            "Epoch 42: val_loss did not improve from 0.08867\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9698 - loss: 0.1025 - val_accuracy: 0.9950 - val_loss: 0.0907\n",
            "Epoch 43/100\n",
            "\u001b[1m41/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9685 - loss: 0.1110  \n",
            "Epoch 43: val_loss did not improve from 0.08867\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9674 - loss: 0.1117 - val_accuracy: 0.9350 - val_loss: 0.1242\n",
            "Epoch 44/100\n",
            "\u001b[1m40/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9659 - loss: 0.1165  \n",
            "Epoch 44: val_loss improved from 0.08867 to 0.08667, saving model to /content/drive/My Drive/Colab Notebooks/bestModel.keras\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9659 - loss: 0.1157 - val_accuracy: 0.9800 - val_loss: 0.0867\n",
            "Epoch 45/100\n",
            "\u001b[1m39/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9530 - loss: 0.1182  \n",
            "Epoch 45: val_loss did not improve from 0.08667\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9544 - loss: 0.1166 - val_accuracy: 0.9850 - val_loss: 0.0879\n",
            "Epoch 46/100\n",
            "\u001b[1m24/50\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9730 - loss: 0.1033 \n",
            "Epoch 46: val_loss improved from 0.08667 to 0.08159, saving model to /content/drive/My Drive/Colab Notebooks/bestModel.keras\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9713 - loss: 0.1022 - val_accuracy: 0.9850 - val_loss: 0.0816\n",
            "Epoch 47/100\n",
            "\u001b[1m38/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9723 - loss: 0.0971 \n",
            "Epoch 47: val_loss did not improve from 0.08159\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9724 - loss: 0.0973 - val_accuracy: 0.9850 - val_loss: 0.0873\n",
            "Epoch 48/100\n",
            "\u001b[1m38/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9671 - loss: 0.1004 \n",
            "Epoch 48: val_loss did not improve from 0.08159\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9667 - loss: 0.1020 - val_accuracy: 0.9700 - val_loss: 0.0855\n",
            "Epoch 49/100\n",
            "\u001b[1m40/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9632 - loss: 0.1039  \n",
            "Epoch 49: val_loss did not improve from 0.08159\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9635 - loss: 0.1036 - val_accuracy: 0.9950 - val_loss: 0.0847\n",
            "Epoch 50/100\n",
            "\u001b[1m25/50\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9829 - loss: 0.0805  \n",
            "Epoch 50: val_loss improved from 0.08159 to 0.07781, saving model to /content/drive/My Drive/Colab Notebooks/bestModel.keras\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9765 - loss: 0.0895 - val_accuracy: 0.9750 - val_loss: 0.0778\n",
            "Epoch 51/100\n",
            "\u001b[1m40/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9678 - loss: 0.1041  \n",
            "Epoch 51: val_loss did not improve from 0.07781\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9683 - loss: 0.1029 - val_accuracy: 0.9800 - val_loss: 0.0802\n",
            "Epoch 52/100\n",
            "\u001b[1m41/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9777 - loss: 0.0812  \n",
            "Epoch 52: val_loss improved from 0.07781 to 0.07739, saving model to /content/drive/My Drive/Colab Notebooks/bestModel.keras\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9764 - loss: 0.0836 - val_accuracy: 0.9800 - val_loss: 0.0774\n",
            "Epoch 53/100\n",
            "\u001b[1m41/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9779 - loss: 0.0878 \n",
            "Epoch 53: val_loss did not improve from 0.07739\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9766 - loss: 0.0896 - val_accuracy: 0.9900 - val_loss: 0.0803\n",
            "Epoch 54/100\n",
            "\u001b[1m40/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9704 - loss: 0.0947 \n",
            "Epoch 54: val_loss improved from 0.07739 to 0.07721, saving model to /content/drive/My Drive/Colab Notebooks/bestModel.keras\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9705 - loss: 0.0945 - val_accuracy: 0.9700 - val_loss: 0.0772\n",
            "Epoch 55/100\n",
            "\u001b[1m30/50\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9763 - loss: 0.0872 \n",
            "Epoch 55: val_loss did not improve from 0.07721\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9744 - loss: 0.0903 - val_accuracy: 0.9600 - val_loss: 0.1019\n",
            "Epoch 56/100\n",
            "\u001b[1m40/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9738 - loss: 0.0983 \n",
            "Epoch 56: val_loss did not improve from 0.07721\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9741 - loss: 0.0964 - val_accuracy: 0.9850 - val_loss: 0.0866\n",
            "Epoch 57/100\n",
            "\u001b[1m38/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9584 - loss: 0.1105 \n",
            "Epoch 57: val_loss improved from 0.07721 to 0.07166, saving model to /content/drive/My Drive/Colab Notebooks/bestModel.keras\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9606 - loss: 0.1057 - val_accuracy: 0.9900 - val_loss: 0.0717\n",
            "Epoch 58/100\n",
            "\u001b[1m39/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9547 - loss: 0.1088  \n",
            "Epoch 58: val_loss did not improve from 0.07166\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9564 - loss: 0.1066 - val_accuracy: 0.9350 - val_loss: 0.1226\n",
            "Epoch 59/100\n",
            "\u001b[1m38/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9745 - loss: 0.0955 \n",
            "Epoch 59: val_loss improved from 0.07166 to 0.06768, saving model to /content/drive/My Drive/Colab Notebooks/bestModel.keras\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9729 - loss: 0.0949 - val_accuracy: 0.9850 - val_loss: 0.0677\n",
            "Epoch 60/100\n",
            "\u001b[1m35/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9732 - loss: 0.0844  \n",
            "Epoch 60: val_loss did not improve from 0.06768\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9720 - loss: 0.0858 - val_accuracy: 0.9800 - val_loss: 0.0706\n",
            "Epoch 61/100\n",
            "\u001b[1m41/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9624 - loss: 0.0952 \n",
            "Epoch 61: val_loss did not improve from 0.06768\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9635 - loss: 0.0940 - val_accuracy: 0.9900 - val_loss: 0.0678\n",
            "Epoch 62/100\n",
            "\u001b[1m41/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9764 - loss: 0.0787 \n",
            "Epoch 62: val_loss did not improve from 0.06768\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9759 - loss: 0.0811 - val_accuracy: 0.9700 - val_loss: 0.0733\n",
            "Epoch 63/100\n",
            "\u001b[1m41/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9615 - loss: 0.0933 \n",
            "Epoch 63: val_loss did not improve from 0.06768\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9644 - loss: 0.0913 - val_accuracy: 0.9900 - val_loss: 0.0696\n",
            "Epoch 64/100\n",
            "\u001b[1m42/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9851 - loss: 0.0672 \n",
            "Epoch 64: val_loss did not improve from 0.06768\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9835 - loss: 0.0700 - val_accuracy: 0.9650 - val_loss: 0.0953\n",
            "Epoch 65/100\n",
            "\u001b[1m36/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9759 - loss: 0.0836\n",
            "Epoch 65: val_loss did not improve from 0.06768\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9748 - loss: 0.0838 - val_accuracy: 0.9750 - val_loss: 0.0770\n",
            "Epoch 66/100\n",
            "\u001b[1m49/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9619 - loss: 0.0882\n",
            "Epoch 66: val_loss improved from 0.06768 to 0.06449, saving model to /content/drive/My Drive/Colab Notebooks/bestModel.keras\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9623 - loss: 0.0879 - val_accuracy: 0.9900 - val_loss: 0.0645\n",
            "Epoch 67/100\n",
            "\u001b[1m41/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9725 - loss: 0.0842\n",
            "Epoch 67: val_loss did not improve from 0.06449\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9720 - loss: 0.0854 - val_accuracy: 0.9950 - val_loss: 0.0734\n",
            "Epoch 68/100\n",
            "\u001b[1m49/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9681 - loss: 0.0904\n",
            "Epoch 68: val_loss improved from 0.06449 to 0.06343, saving model to /content/drive/My Drive/Colab Notebooks/bestModel.keras\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9680 - loss: 0.0904 - val_accuracy: 0.9950 - val_loss: 0.0634\n",
            "Epoch 69/100\n",
            "\u001b[1m38/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9723 - loss: 0.0771\n",
            "Epoch 69: val_loss did not improve from 0.06343\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9713 - loss: 0.0779 - val_accuracy: 0.9600 - val_loss: 0.0796\n",
            "Epoch 70/100\n",
            "\u001b[1m43/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9783 - loss: 0.0789\n",
            "Epoch 70: val_loss improved from 0.06343 to 0.06145, saving model to /content/drive/My Drive/Colab Notebooks/bestModel.keras\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9778 - loss: 0.0790 - val_accuracy: 0.9850 - val_loss: 0.0614\n",
            "Epoch 71/100\n",
            "\u001b[1m45/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9603 - loss: 0.1058\n",
            "Epoch 71: val_loss did not improve from 0.06145\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9610 - loss: 0.1041 - val_accuracy: 0.9750 - val_loss: 0.0631\n",
            "Epoch 72/100\n",
            "\u001b[1m46/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9844 - loss: 0.0734\n",
            "Epoch 72: val_loss did not improve from 0.06145\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9838 - loss: 0.0738 - val_accuracy: 0.9900 - val_loss: 0.0774\n",
            "Epoch 73/100\n",
            "\u001b[1m48/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9766 - loss: 0.0829\n",
            "Epoch 73: val_loss improved from 0.06145 to 0.05569, saving model to /content/drive/My Drive/Colab Notebooks/bestModel.keras\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9765 - loss: 0.0827 - val_accuracy: 0.9950 - val_loss: 0.0557\n",
            "Epoch 74/100\n",
            "\u001b[1m40/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9766 - loss: 0.0748\n",
            "Epoch 74: val_loss did not improve from 0.05569\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9751 - loss: 0.0760 - val_accuracy: 1.0000 - val_loss: 0.0585\n",
            "Epoch 75/100\n",
            "\u001b[1m38/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9766 - loss: 0.0792\n",
            "Epoch 75: val_loss did not improve from 0.05569\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9754 - loss: 0.0824 - val_accuracy: 0.9900 - val_loss: 0.0595\n",
            "Epoch 76/100\n",
            "\u001b[1m39/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9750 - loss: 0.0786\n",
            "Epoch 76: val_loss did not improve from 0.05569\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9745 - loss: 0.0794 - val_accuracy: 0.9700 - val_loss: 0.0816\n",
            "Epoch 77/100\n",
            "\u001b[1m41/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9721 - loss: 0.0798\n",
            "Epoch 77: val_loss did not improve from 0.05569\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9722 - loss: 0.0806 - val_accuracy: 0.9700 - val_loss: 0.0701\n",
            "Epoch 78/100\n",
            "\u001b[1m37/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9686 - loss: 0.0893\n",
            "Epoch 78: val_loss did not improve from 0.05569\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9677 - loss: 0.0917 - val_accuracy: 1.0000 - val_loss: 0.0576\n",
            "Epoch 79/100\n",
            "\u001b[1m42/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9820 - loss: 0.0701\n",
            "Epoch 79: val_loss did not improve from 0.05569\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9806 - loss: 0.0723 - val_accuracy: 0.9950 - val_loss: 0.0586\n",
            "Epoch 80/100\n",
            "\u001b[1m42/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9604 - loss: 0.0998\n",
            "Epoch 80: val_loss did not improve from 0.05569\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9632 - loss: 0.0956 - val_accuracy: 0.9800 - val_loss: 0.0592\n",
            "Epoch 81/100\n",
            "\u001b[1m34/50\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9628 - loss: 0.0777  \n",
            "Epoch 81: val_loss did not improve from 0.05569\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9656 - loss: 0.0768 - val_accuracy: 0.9500 - val_loss: 0.0841\n",
            "Epoch 82/100\n",
            "\u001b[1m30/50\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9834 - loss: 0.0726  \n",
            "Epoch 82: val_loss did not improve from 0.05569\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9825 - loss: 0.0738 - val_accuracy: 0.9750 - val_loss: 0.0681\n",
            "Epoch 83/100\n",
            "\u001b[1m39/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9734 - loss: 0.0768  \n",
            "Epoch 83: val_loss did not improve from 0.05569\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9720 - loss: 0.0787 - val_accuracy: 0.9900 - val_loss: 0.0643\n",
            "Epoch 84/100\n",
            "\u001b[1m40/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9811 - loss: 0.0700  \n",
            "Epoch 84: val_loss did not improve from 0.05569\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9808 - loss: 0.0700 - val_accuracy: 0.9950 - val_loss: 0.0649\n",
            "Epoch 85/100\n",
            "\u001b[1m39/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9772 - loss: 0.0723 \n",
            "Epoch 85: val_loss did not improve from 0.05569\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9763 - loss: 0.0730 - val_accuracy: 0.9350 - val_loss: 0.1039\n",
            "Epoch 86/100\n",
            "\u001b[1m32/50\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9790 - loss: 0.0673  \n",
            "Epoch 86: val_loss improved from 0.05569 to 0.05188, saving model to /content/drive/My Drive/Colab Notebooks/bestModel.keras\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9743 - loss: 0.0759 - val_accuracy: 1.0000 - val_loss: 0.0519\n",
            "Epoch 87/100\n",
            "\u001b[1m37/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9745 - loss: 0.0796 \n",
            "Epoch 87: val_loss improved from 0.05188 to 0.05004, saving model to /content/drive/My Drive/Colab Notebooks/bestModel.keras\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9763 - loss: 0.0769 - val_accuracy: 0.9950 - val_loss: 0.0500\n",
            "Epoch 88/100\n",
            "\u001b[1m40/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9843 - loss: 0.0689  \n",
            "Epoch 88: val_loss did not improve from 0.05004\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9841 - loss: 0.0681 - val_accuracy: 0.9850 - val_loss: 0.0543\n",
            "Epoch 89/100\n",
            "\u001b[1m39/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9804 - loss: 0.0690 \n",
            "Epoch 89: val_loss did not improve from 0.05004\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9793 - loss: 0.0697 - val_accuracy: 0.9650 - val_loss: 0.0613\n",
            "Epoch 90/100\n",
            "\u001b[1m33/50\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9703 - loss: 0.0882 \n",
            "Epoch 90: val_loss did not improve from 0.05004\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9714 - loss: 0.0846 - val_accuracy: 1.0000 - val_loss: 0.0607\n",
            "Epoch 91/100\n",
            "\u001b[1m29/50\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9689 - loss: 0.0804 \n",
            "Epoch 91: val_loss did not improve from 0.05004\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9720 - loss: 0.0752 - val_accuracy: 0.9900 - val_loss: 0.0587\n",
            "Epoch 92/100\n",
            "\u001b[1m36/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9647 - loss: 0.0811  \n",
            "Epoch 92: val_loss did not improve from 0.05004\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9679 - loss: 0.0770 - val_accuracy: 0.9700 - val_loss: 0.0705\n",
            "Epoch 93/100\n",
            "\u001b[1m37/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9665 - loss: 0.0923  \n",
            "Epoch 93: val_loss did not improve from 0.05004\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9669 - loss: 0.0921 - val_accuracy: 0.9900 - val_loss: 0.0542\n",
            "Epoch 94/100\n",
            "\u001b[1m29/50\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9763 - loss: 0.0656  \n",
            "Epoch 94: val_loss did not improve from 0.05004\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9755 - loss: 0.0691 - val_accuracy: 0.9950 - val_loss: 0.0523\n",
            "Epoch 95/100\n",
            "\u001b[1m33/50\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9812 - loss: 0.0708  \n",
            "Epoch 95: val_loss did not improve from 0.05004\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9783 - loss: 0.0732 - val_accuracy: 0.9850 - val_loss: 0.0511\n",
            "Epoch 96/100\n",
            "\u001b[1m38/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9785 - loss: 0.0736  \n",
            "Epoch 96: val_loss did not improve from 0.05004\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9784 - loss: 0.0726 - val_accuracy: 0.9900 - val_loss: 0.0503\n",
            "Epoch 97/100\n",
            "\u001b[1m38/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9868 - loss: 0.0593 \n",
            "Epoch 97: val_loss improved from 0.05004 to 0.04813, saving model to /content/drive/My Drive/Colab Notebooks/bestModel.keras\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9857 - loss: 0.0603 - val_accuracy: 0.9950 - val_loss: 0.0481\n",
            "Epoch 98/100\n",
            "\u001b[1m29/50\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9649 - loss: 0.0853  \n",
            "Epoch 98: val_loss improved from 0.04813 to 0.04722, saving model to /content/drive/My Drive/Colab Notebooks/bestModel.keras\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9711 - loss: 0.0757 - val_accuracy: 0.9950 - val_loss: 0.0472\n",
            "Epoch 99/100\n",
            "\u001b[1m35/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9778 - loss: 0.0747 \n",
            "Epoch 99: val_loss did not improve from 0.04722\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9753 - loss: 0.0759 - val_accuracy: 0.9850 - val_loss: 0.0480\n",
            "Epoch 100/100\n",
            "\u001b[1m36/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9635 - loss: 0.0784 \n",
            "Epoch 100: val_loss did not improve from 0.04722\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9671 - loss: 0.0754 - val_accuracy: 0.9650 - val_loss: 0.0779\n",
            "Best validation loss: 0.04722297191619873\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(learn.history['accuracy'])\n",
        "plt.plot(learn.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",
        "epoch_count = len(learn.history['accuracy'])  # Total epochs\n",
        "plt.savefig(f'/content/drive/My Drive/Colab Notebooks/accuracy_epoch{epoch_count}.png')"
      ],
      "metadata": {
        "id": "bw6fM0_1ssbt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "82229be1-b731-4efe-e093-b04e2c9df594"
      },
      "execution_count": 237,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot training & validation loss values\n",
        "plt.plot(learn.history['loss'])\n",
        "plt.plot(learn.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",
        "epoch_count = len(learn.history['loss'])\n",
        "plt.savefig(f'/content/drive/My Drive/Colab Notebooks/loss_epoch{epoch_count}.png')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "asXUzzLitnjs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "6a786e0d-6958-4186-b731-221eaa1389eb"
      },
      "execution_count": 238,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_hat = model.predict(X_test)\n",
        "y_hat_classes = np.argmax(y_hat, axis=1)\n",
        "accuracy = accuracy_score(y_test, y_hat_classes)\n",
        "cm = confusion_matrix(y_test, y_hat_classes)\n",
        "print(\"Accuracy: \", accuracy)"
      ],
      "metadata": {
        "id": "Gq10ZKnDvNMc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "44d6ba7d-7ee0-4e04-e243-e578932c8e27"
      },
      "execution_count": 239,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step \n",
            "Accuracy:  0.99\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Confusion Matrix:\\n\")\n",
        "figure, ax = plot_confusion_matrix(conf_mat=cm,\n",
        "                                   class_names=['Low', 'Medium', 'High'],\n",
        "                                   figsize=(6,6),\n",
        "                                   colorbar=True)"
      ],
      "metadata": {
        "id": "Ss1v6gxvvqzc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 567
        },
        "outputId": "1fb3f2dc-3a48-48e4-901a-402239f3068e"
      },
      "execution_count": 240,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix:\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.save('stress-predictor-model-epoch-100.keras')\n",
        "model.save('/content/drive/My Drive/Colab Notebooks/stress-predictor-model-epoch-100.keras')"
      ],
      "metadata": {
        "id": "KUpLk3_Vv1U0"
      },
      "execution_count": 241,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = [30, 50, 70, 100]\n",
        "y = [0.95, 0.93, 0.955, 0.99]\n",
        "\n",
        "plt.figure(figsize=(8, 5))\n",
        "plt.plot(x, y, marker='o', linestyle='-', color='b', label='Accuracy')\n",
        "\n",
        "plt.title('Accuracy vs Data Points', fontsize=14)\n",
        "plt.xlabel('Data Points', fontsize=12)\n",
        "plt.ylabel('Accuracy', fontsize=12)\n",
        "plt.grid(True, linestyle='--', alpha=0.7)\n",
        "plt.xticks(x)\n",
        "plt.yticks([0.90, 0.92, 0.94, 0.96, 0.98, 1])\n",
        "plt.legend()\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "c8zC90102o_p",
        "outputId": "3483ca4a-f2f0-4d1e-d436-f2c033716ad5"
      },
      "execution_count": 243,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}