stress_predictor_model.ipynb

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        "<a href=\"https://colab.research.google.com/github/VSUrhuel/vsu-stress-predictor/blob/master/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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      "metadata": {
        "id": "dcnJXZ6mlGEe"
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      "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": null,
      "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": "5cb1ceec-d13d-43e1-f50d-6f902beb477e"
      },
      "execution_count": null,
      "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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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
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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": 54
        }
      ]
    },
    {
      "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": null,
      "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": null,
      "outputs": []
    },
    {
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      "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": null,
      "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": null,
      "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",
        ")"
      ],
      "metadata": {
        "id": "U-1a8lcFrEaP"
      },
      "execution_count": null,
      "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": "16c13ac3-deba-4733-f8b9-eca519f21bda"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "\u001b[1m43/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.5579 - loss: 1.1087\n",
            "Epoch 1: val_loss improved from inf to 0.76894, 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[1m2s\u001b[0m 16ms/step - accuracy: 0.5687 - loss: 1.0755 - val_accuracy: 0.7650 - val_loss: 0.7689\n",
            "Epoch 2/100\n",
            "\u001b[1m31/50\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6998 - loss: 0.7066\n",
            "Epoch 2: val_loss improved from 0.76894 to 0.61507, 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: 0.7008 - loss: 0.7005 - val_accuracy: 0.8250 - val_loss: 0.6151\n",
            "Epoch 3/100\n",
            "\u001b[1m46/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.7706 - loss: 0.5912\n",
            "Epoch 3: val_loss improved from 0.61507 to 0.53454, 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: 0.7687 - loss: 0.5918 - val_accuracy: 0.7500 - val_loss: 0.5345\n",
            "Epoch 4/100\n",
            "\u001b[1m43/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - accuracy: 0.7629 - loss: 0.5504\n",
            "Epoch 4: val_loss improved from 0.53454 to 0.49260, 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 15ms/step - accuracy: 0.7661 - loss: 0.5461 - val_accuracy: 0.8100 - val_loss: 0.4926\n",
            "Epoch 5/100\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.8052 - loss: 0.4812\n",
            "Epoch 5: val_loss improved from 0.49260 to 0.42661, 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 15ms/step - accuracy: 0.8051 - loss: 0.4812 - val_accuracy: 0.8750 - val_loss: 0.4266\n",
            "Epoch 6/100\n",
            "\u001b[1m49/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - accuracy: 0.8263 - loss: 0.4436\n",
            "Epoch 6: val_loss improved from 0.42661 to 0.38424, 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[1m2s\u001b[0m 30ms/step - accuracy: 0.8263 - loss: 0.4435 - val_accuracy: 0.8700 - val_loss: 0.3842\n",
            "Epoch 7/100\n",
            "\u001b[1m49/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.8419 - loss: 0.4118\n",
            "Epoch 7: val_loss improved from 0.38424 to 0.35665, 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[1m2s\u001b[0m 27ms/step - accuracy: 0.8417 - loss: 0.4116 - val_accuracy: 0.9050 - val_loss: 0.3566\n",
            "Epoch 8/100\n",
            "\u001b[1m47/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - accuracy: 0.8469 - loss: 0.4002\n",
            "Epoch 8: val_loss did not improve from 0.35665\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 21ms/step - accuracy: 0.8473 - loss: 0.3992 - val_accuracy: 0.8600 - val_loss: 0.3839\n",
            "Epoch 9/100\n",
            "\u001b[1m49/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.8479 - loss: 0.3798 \n",
            "Epoch 9: val_loss improved from 0.35665 to 0.31909, 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 15ms/step - accuracy: 0.8485 - loss: 0.3793 - val_accuracy: 0.9050 - val_loss: 0.3191\n",
            "Epoch 10/100\n",
            "\u001b[1m48/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - accuracy: 0.8677 - loss: 0.3551\n",
            "Epoch 10: val_loss improved from 0.31909 to 0.30648, 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 14ms/step - accuracy: 0.8680 - loss: 0.3546 - val_accuracy: 0.9050 - val_loss: 0.3065\n",
            "Epoch 11/100\n",
            "\u001b[1m43/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8557 - loss: 0.3665\n",
            "Epoch 11: val_loss improved from 0.30648 to 0.27896, 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: 0.8591 - loss: 0.3611 - val_accuracy: 0.9350 - val_loss: 0.2790\n",
            "Epoch 12/100\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - accuracy: 0.8842 - loss: 0.3182\n",
            "Epoch 12: val_loss improved from 0.27896 to 0.27094, 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 19ms/step - accuracy: 0.8842 - loss: 0.3181 - val_accuracy: 0.9250 - val_loss: 0.2709\n",
            "Epoch 13/100\n",
            "\u001b[1m48/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.8846 - loss: 0.3035\n",
            "Epoch 13: val_loss improved from 0.27094 to 0.25419, 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: 0.8849 - loss: 0.3033 - val_accuracy: 0.9200 - val_loss: 0.2542\n",
            "Epoch 14/100\n",
            "\u001b[1m37/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8937 - loss: 0.2952\n",
            "Epoch 14: val_loss improved from 0.25419 to 0.24527, 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: 0.8938 - loss: 0.2940 - val_accuracy: 0.9250 - val_loss: 0.2453\n",
            "Epoch 15/100\n",
            "\u001b[1m34/50\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9090 - loss: 0.2783\n",
            "Epoch 15: val_loss improved from 0.24527 to 0.22889, 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: 0.9072 - loss: 0.2778 - val_accuracy: 0.9450 - val_loss: 0.2289\n",
            "Epoch 16/100\n",
            "\u001b[1m48/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9060 - loss: 0.2587\n",
            "Epoch 16: val_loss did not improve from 0.22889\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9062 - loss: 0.2589 - val_accuracy: 0.9200 - val_loss: 0.2317\n",
            "Epoch 17/100\n",
            "\u001b[1m41/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8998 - loss: 0.2505\n",
            "Epoch 17: val_loss improved from 0.22889 to 0.21913, 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: 0.9006 - loss: 0.2503 - val_accuracy: 0.9200 - val_loss: 0.2191\n",
            "Epoch 18/100\n",
            "\u001b[1m40/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9163 - loss: 0.2394\n",
            "Epoch 18: val_loss improved from 0.21913 to 0.19917, 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: 0.9173 - loss: 0.2382 - val_accuracy: 0.9600 - val_loss: 0.1992\n",
            "Epoch 19/100\n",
            "\u001b[1m47/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9275 - loss: 0.2282\n",
            "Epoch 19: val_loss did not improve from 0.19917\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.9274 - loss: 0.2280 - val_accuracy: 0.9250 - val_loss: 0.2265\n",
            "Epoch 20/100\n",
            "\u001b[1m42/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9247 - loss: 0.2219\n",
            "Epoch 20: val_loss improved from 0.19917 to 0.18809, 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: 0.9256 - loss: 0.2207 - val_accuracy: 0.9300 - val_loss: 0.1881\n",
            "Epoch 21/100\n",
            "\u001b[1m44/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9070 - loss: 0.2254\n",
            "Epoch 21: val_loss improved from 0.18809 to 0.18266, 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: 0.9099 - loss: 0.2234 - val_accuracy: 0.9350 - val_loss: 0.1827\n",
            "Epoch 22/100\n",
            "\u001b[1m47/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9394 - loss: 0.1947\n",
            "Epoch 22: val_loss did not improve from 0.18266\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.9392 - loss: 0.1947 - val_accuracy: 0.9300 - val_loss: 0.1862\n",
            "Epoch 23/100\n",
            "\u001b[1m42/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9214 - loss: 0.2034\n",
            "Epoch 23: val_loss improved from 0.18266 to 0.16854, 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: 0.9232 - loss: 0.2017 - val_accuracy: 0.9700 - val_loss: 0.1685\n",
            "Epoch 24/100\n",
            "\u001b[1m44/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9358 - loss: 0.1865\n",
            "Epoch 24: val_loss did not improve from 0.16854\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9354 - loss: 0.1871 - val_accuracy: 0.9600 - val_loss: 0.1696\n",
            "Epoch 25/100\n",
            "\u001b[1m36/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9463 - loss: 0.1815\n",
            "Epoch 25: val_loss improved from 0.16854 to 0.15561, 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: 0.9450 - loss: 0.1819 - val_accuracy: 0.9550 - val_loss: 0.1556\n",
            "Epoch 26/100\n",
            "\u001b[1m45/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9513 - loss: 0.1699\n",
            "Epoch 26: val_loss improved from 0.15561 to 0.15208, 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: 0.9499 - loss: 0.1709 - val_accuracy: 0.9600 - val_loss: 0.1521\n",
            "Epoch 27/100\n",
            "\u001b[1m28/50\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9446 - loss: 0.1765\n",
            "Epoch 27: val_loss improved from 0.15208 to 0.14942, 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: 0.9429 - loss: 0.1740 - val_accuracy: 0.9850 - val_loss: 0.1494\n",
            "Epoch 28/100\n",
            "\u001b[1m39/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9505 - loss: 0.1719\n",
            "Epoch 28: val_loss improved from 0.14942 to 0.14168, 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: 0.9504 - loss: 0.1695 - val_accuracy: 0.9800 - val_loss: 0.1417\n",
            "Epoch 29/100\n",
            "\u001b[1m48/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9386 - loss: 0.1722\n",
            "Epoch 29: val_loss did not improve from 0.14168\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9390 - loss: 0.1718 - val_accuracy: 0.9350 - val_loss: 0.1586\n",
            "Epoch 30/100\n",
            "\u001b[1m38/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9415 - loss: 0.1648\n",
            "Epoch 30: val_loss did not improve from 0.14168\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9419 - loss: 0.1635 - val_accuracy: 0.9250 - val_loss: 0.1514\n",
            "Epoch 31/100\n",
            "\u001b[1m41/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9532 - loss: 0.1549\n",
            "Epoch 31: val_loss did not improve from 0.14168\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9531 - loss: 0.1551 - val_accuracy: 0.9500 - val_loss: 0.1739\n",
            "Epoch 32/100\n",
            "\u001b[1m49/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.9397 - loss: 0.1522 \n",
            "Epoch 32: val_loss improved from 0.14168 to 0.13579, 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 15ms/step - accuracy: 0.9401 - loss: 0.1521 - val_accuracy: 0.9650 - val_loss: 0.1358\n",
            "Epoch 33/100\n",
            "\u001b[1m39/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9314 - loss: 0.1661\n",
            "Epoch 33: val_loss improved from 0.13579 to 0.13299, 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: 0.9349 - loss: 0.1636 - val_accuracy: 0.9700 - val_loss: 0.1330\n",
            "Epoch 34/100\n",
            "\u001b[1m49/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - accuracy: 0.9643 - loss: 0.1239\n",
            "Epoch 34: val_loss did not improve from 0.13299\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.9641 - loss: 0.1244 - val_accuracy: 0.9300 - val_loss: 0.1639\n",
            "Epoch 35/100\n",
            "\u001b[1m49/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - accuracy: 0.9609 - loss: 0.1348\n",
            "Epoch 35: val_loss improved from 0.13299 to 0.12816, 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 16ms/step - accuracy: 0.9607 - loss: 0.1350 - val_accuracy: 0.9800 - val_loss: 0.1282\n",
            "Epoch 36/100\n",
            "\u001b[1m38/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9650 - loss: 0.1230\n",
            "Epoch 36: val_loss did not improve from 0.12816\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9634 - loss: 0.1269 - val_accuracy: 0.9400 - val_loss: 0.1629\n",
            "Epoch 37/100\n",
            "\u001b[1m40/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9613 - loss: 0.1200\n",
            "Epoch 37: val_loss did not improve from 0.12816\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9603 - loss: 0.1223 - val_accuracy: 0.9600 - val_loss: 0.1463\n",
            "Epoch 38/100\n",
            "\u001b[1m36/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9543 - loss: 0.1364 \n",
            "Epoch 38: val_loss did not improve from 0.12816\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9561 - loss: 0.1337 - val_accuracy: 0.9550 - val_loss: 0.1305\n",
            "Epoch 39/100\n",
            "\u001b[1m39/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9725 - loss: 0.1097  \n",
            "Epoch 39: val_loss improved from 0.12816 to 0.12461, 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: 0.9696 - loss: 0.1144 - val_accuracy: 0.9500 - val_loss: 0.1246\n",
            "Epoch 40/100\n",
            "\u001b[1m32/50\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9529 - loss: 0.1256  \n",
            "Epoch 40: val_loss did not improve from 0.12461\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9543 - loss: 0.1251 - val_accuracy: 0.9600 - val_loss: 0.1250\n",
            "Epoch 41/100\n",
            "\u001b[1m39/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9637 - loss: 0.1245  \n",
            "Epoch 41: val_loss improved from 0.12461 to 0.12146, 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: 0.1258 - val_accuracy: 0.9700 - val_loss: 0.1215\n",
            "Epoch 42/100\n",
            "\u001b[1m25/50\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9529 - loss: 0.1294  \n",
            "Epoch 42: val_loss did not improve from 0.12146\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9543 - loss: 0.1280 - val_accuracy: 0.9550 - val_loss: 0.1259\n",
            "Epoch 43/100\n",
            "\u001b[1m34/50\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9395 - loss: 0.1411  \n",
            "Epoch 43: val_loss did not improve from 0.12146\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9426 - loss: 0.1392 - val_accuracy: 0.9100 - val_loss: 0.1599\n",
            "Epoch 44/100\n",
            "\u001b[1m37/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9592 - loss: 0.1198  \n",
            "Epoch 44: val_loss did not improve from 0.12146\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9601 - loss: 0.1202 - val_accuracy: 0.9400 - val_loss: 0.1352\n",
            "Epoch 45/100\n",
            "\u001b[1m41/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9560 - loss: 0.1216 \n",
            "Epoch 45: val_loss improved from 0.12146 to 0.11977, 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: 0.9557 - loss: 0.1217 - val_accuracy: 0.9750 - val_loss: 0.1198\n",
            "Epoch 46/100\n",
            "\u001b[1m41/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9712 - loss: 0.1087 \n",
            "Epoch 46: val_loss improved from 0.11977 to 0.11933, 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: 0.9694 - loss: 0.1107 - val_accuracy: 0.9500 - val_loss: 0.1193\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: 0.9642 - loss: 0.1170  \n",
            "Epoch 47: val_loss improved from 0.11933 to 0.11457, 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: 0.9639 - loss: 0.1163 - val_accuracy: 0.9600 - val_loss: 0.1146\n",
            "Epoch 48/100\n",
            "\u001b[1m39/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9578 - loss: 0.1232  \n",
            "Epoch 48: val_loss improved from 0.11457 to 0.11158, 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: 0.9587 - loss: 0.1221 - val_accuracy: 0.9550 - val_loss: 0.1116\n",
            "Epoch 49/100\n",
            "\u001b[1m36/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9548 - loss: 0.1194  \n",
            "Epoch 49: val_loss did not improve from 0.11158\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9554 - loss: 0.1179 - val_accuracy: 0.9600 - val_loss: 0.1316\n",
            "Epoch 50/100\n",
            "\u001b[1m36/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9328 - loss: 0.1349  \n",
            "Epoch 50: val_loss did not improve from 0.11158\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9396 - loss: 0.1293 - val_accuracy: 0.9600 - val_loss: 0.1163\n",
            "Epoch 51/100\n",
            "\u001b[1m27/50\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9438 - loss: 0.1216  \n",
            "Epoch 51: val_loss did not improve from 0.11158\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9481 - loss: 0.1177 - val_accuracy: 0.9450 - val_loss: 0.1184\n",
            "Epoch 52/100\n",
            "\u001b[1m34/50\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9525 - loss: 0.1320  \n",
            "Epoch 52: val_loss did not improve from 0.11158\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9559 - loss: 0.1259 - val_accuracy: 0.9500 - val_loss: 0.1157\n",
            "Epoch 53/100\n",
            "\u001b[1m25/50\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9670 - loss: 0.0996  \n",
            "Epoch 53: val_loss improved from 0.11158 to 0.10314, 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: 0.9681 - loss: 0.1015 - val_accuracy: 0.9750 - val_loss: 0.1031\n",
            "Epoch 54/100\n",
            "\u001b[1m48/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9655 - loss: 0.0943\n",
            "Epoch 54: val_loss improved from 0.10314 to 0.10099, 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: 0.9653 - loss: 0.0947 - val_accuracy: 0.9800 - val_loss: 0.1010\n",
            "Epoch 55/100\n",
            "\u001b[1m31/50\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9614 - loss: 0.1047  \n",
            "Epoch 55: val_loss did not improve from 0.10099\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9629 - loss: 0.1032 - val_accuracy: 0.9500 - val_loss: 0.1110\n",
            "Epoch 56/100\n",
            "\u001b[1m30/50\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9706 - loss: 0.0930  \n",
            "Epoch 56: val_loss improved from 0.10099 to 0.09614, 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: 0.9704 - loss: 0.0945 - val_accuracy: 0.9750 - val_loss: 0.0961\n",
            "Epoch 57/100\n",
            "\u001b[1m31/50\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9518 - loss: 0.1092  \n",
            "Epoch 57: val_loss did not improve from 0.09614\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9541 - loss: 0.1063 - val_accuracy: 0.9700 - val_loss: 0.1012\n",
            "Epoch 58/100\n",
            "\u001b[1m40/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9737 - loss: 0.0934  \n",
            "Epoch 58: val_loss did not improve from 0.09614\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9722 - loss: 0.0950 - val_accuracy: 0.9700 - val_loss: 0.1252\n",
            "Epoch 59/100\n",
            "\u001b[1m39/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9685 - loss: 0.0874  \n",
            "Epoch 59: val_loss did not improve from 0.09614\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9667 - loss: 0.0915 - val_accuracy: 0.9350 - val_loss: 0.1161\n",
            "Epoch 60/100\n",
            "\u001b[1m39/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9626 - loss: 0.1099  \n",
            "Epoch 60: val_loss did not improve from 0.09614\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9622 - loss: 0.1094 - val_accuracy: 0.9800 - val_loss: 0.0993\n",
            "Epoch 61/100\n",
            "\u001b[1m40/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9613 - loss: 0.0975 \n",
            "Epoch 61: val_loss did not improve from 0.09614\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9616 - loss: 0.0989 - val_accuracy: 0.9750 - val_loss: 0.0970\n",
            "Epoch 62/100\n",
            "\u001b[1m38/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9612 - loss: 0.1064  \n",
            "Epoch 62: val_loss did not improve from 0.09614\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9599 - loss: 0.1071 - val_accuracy: 0.9650 - val_loss: 0.1111\n",
            "Epoch 63/100\n",
            "\u001b[1m43/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9659 - loss: 0.1027 \n",
            "Epoch 63: val_loss did not improve from 0.09614\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9650 - loss: 0.1044 - val_accuracy: 0.9750 - val_loss: 0.1024\n",
            "Epoch 64/100\n",
            "\u001b[1m41/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9654 - loss: 0.0916  \n",
            "Epoch 64: val_loss improved from 0.09614 to 0.09544, 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: 0.9654 - loss: 0.0928 - val_accuracy: 0.9850 - val_loss: 0.0954\n",
            "Epoch 65/100\n",
            "\u001b[1m32/50\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9673 - loss: 0.0971 \n",
            "Epoch 65: val_loss did not improve from 0.09544\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9630 - loss: 0.1041 - val_accuracy: 0.9800 - val_loss: 0.0982\n",
            "Epoch 66/100\n",
            "\u001b[1m38/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9671 - loss: 0.0929  \n",
            "Epoch 66: val_loss improved from 0.09544 to 0.09060, 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: 0.9693 - loss: 0.0915 - val_accuracy: 0.9800 - val_loss: 0.0906\n",
            "Epoch 67/100\n",
            "\u001b[1m41/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9696 - loss: 0.0876  \n",
            "Epoch 67: val_loss did not improve from 0.09060\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9695 - loss: 0.0880 - val_accuracy: 0.9450 - val_loss: 0.1238\n",
            "Epoch 68/100\n",
            "\u001b[1m23/50\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9769 - loss: 0.0790  \n",
            "Epoch 68: val_loss did not improve from 0.09060\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9736 - loss: 0.0853 - val_accuracy: 0.9600 - val_loss: 0.0994\n",
            "Epoch 69/100\n",
            "\u001b[1m34/50\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9784 - loss: 0.0775 \n",
            "Epoch 69: val_loss did not improve from 0.09060\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9776 - loss: 0.0799 - val_accuracy: 0.9800 - val_loss: 0.0906\n",
            "Epoch 70/100\n",
            "\u001b[1m42/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9461 - loss: 0.1158  \n",
            "Epoch 70: val_loss did not improve from 0.09060\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9488 - loss: 0.1123 - val_accuracy: 0.9850 - val_loss: 0.0922\n",
            "Epoch 71/100\n",
            "\u001b[1m39/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9691 - loss: 0.0843 \n",
            "Epoch 71: val_loss did not improve from 0.09060\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9665 - loss: 0.0884 - val_accuracy: 0.9400 - val_loss: 0.1126\n",
            "Epoch 72/100\n",
            "\u001b[1m40/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9736 - loss: 0.0979  \n",
            "Epoch 72: val_loss improved from 0.09060 to 0.08594, 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: 0.9724 - loss: 0.0989 - val_accuracy: 0.9750 - val_loss: 0.0859\n",
            "Epoch 73/100\n",
            "\u001b[1m30/50\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9722 - loss: 0.0907  \n",
            "Epoch 73: val_loss did not improve from 0.08594\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9722 - loss: 0.0889 - val_accuracy: 0.9850 - val_loss: 0.0874\n",
            "Epoch 74/100\n",
            "\u001b[1m41/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9702 - loss: 0.0798\n",
            "Epoch 74: val_loss did not improve from 0.08594\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9703 - loss: 0.0808 - val_accuracy: 0.9450 - val_loss: 0.1258\n",
            "Epoch 75/100\n",
            "\u001b[1m37/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9609 - loss: 0.1035\n",
            "Epoch 75: val_loss did not improve from 0.08594\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9629 - loss: 0.0994 - val_accuracy: 0.9500 - val_loss: 0.1077\n",
            "Epoch 76/100\n",
            "\u001b[1m49/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9793 - loss: 0.0792\n",
            "Epoch 76: val_loss did not improve from 0.08594\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9791 - loss: 0.0793 - val_accuracy: 0.9700 - val_loss: 0.0900\n",
            "Epoch 77/100\n",
            "\u001b[1m39/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9761 - loss: 0.0769\n",
            "Epoch 77: val_loss did not improve from 0.08594\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9750 - loss: 0.0794 - val_accuracy: 0.9700 - val_loss: 0.1012\n",
            "Epoch 78/100\n",
            "\u001b[1m46/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9746 - loss: 0.0903\n",
            "Epoch 78: val_loss did not improve from 0.08594\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9739 - loss: 0.0905 - val_accuracy: 0.9750 - val_loss: 0.0927\n",
            "Epoch 79/100\n",
            "\u001b[1m47/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9676 - loss: 0.0976\n",
            "Epoch 79: val_loss did not improve from 0.08594\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9678 - loss: 0.0970 - val_accuracy: 0.9850 - val_loss: 0.0865\n",
            "Epoch 80/100\n",
            "\u001b[1m42/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9778 - loss: 0.0784\n",
            "Epoch 80: val_loss did not improve from 0.08594\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9762 - loss: 0.0809 - val_accuracy: 0.9650 - val_loss: 0.1000\n",
            "Epoch 81/100\n",
            "\u001b[1m46/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9773 - loss: 0.0801\n",
            "Epoch 81: val_loss did not improve from 0.08594\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9767 - loss: 0.0809 - val_accuracy: 0.9600 - val_loss: 0.0993\n",
            "Epoch 82/100\n",
            "\u001b[1m41/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9772 - loss: 0.0747\n",
            "Epoch 82: val_loss did not improve from 0.08594\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9757 - loss: 0.0764 - val_accuracy: 0.9500 - val_loss: 0.1052\n",
            "Epoch 83/100\n",
            "\u001b[1m47/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9682 - loss: 0.0785\n",
            "Epoch 83: val_loss did not improve from 0.08594\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9686 - loss: 0.0784 - val_accuracy: 0.9450 - val_loss: 0.1061\n",
            "Epoch 84/100\n",
            "\u001b[1m44/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9724 - loss: 0.0813\n",
            "Epoch 84: val_loss did not improve from 0.08594\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9722 - loss: 0.0815 - val_accuracy: 0.9450 - val_loss: 0.1023\n",
            "Epoch 85/100\n",
            "\u001b[1m38/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9712 - loss: 0.0797\n",
            "Epoch 85: val_loss did not improve from 0.08594\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9678 - loss: 0.0840 - val_accuracy: 0.9650 - val_loss: 0.0940\n",
            "Epoch 86/100\n",
            "\u001b[1m44/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9793 - loss: 0.0765\n",
            "Epoch 86: val_loss did not improve from 0.08594\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9793 - loss: 0.0767 - val_accuracy: 0.9700 - val_loss: 0.0907\n",
            "Epoch 87/100\n",
            "\u001b[1m40/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9770 - loss: 0.0799\n",
            "Epoch 87: val_loss did not improve from 0.08594\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9770 - loss: 0.0791 - val_accuracy: 0.9500 - val_loss: 0.1045\n",
            "Epoch 88/100\n",
            "\u001b[1m42/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9728 - loss: 0.0761\n",
            "Epoch 88: val_loss did not improve from 0.08594\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9730 - loss: 0.0765 - val_accuracy: 0.9750 - val_loss: 0.0862\n",
            "Epoch 89/100\n",
            "\u001b[1m23/50\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9755 - loss: 0.0822 \n",
            "Epoch 89: val_loss did not improve from 0.08594\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9730 - loss: 0.0837 - val_accuracy: 0.9350 - val_loss: 0.1231\n",
            "Epoch 90/100\n",
            "\u001b[1m29/50\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9758 - loss: 0.0701 \n",
            "Epoch 90: val_loss did not improve from 0.08594\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9759 - loss: 0.0701 - val_accuracy: 0.9400 - val_loss: 0.1054\n",
            "Epoch 91/100\n",
            "\u001b[1m27/50\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9796 - loss: 0.0658  \n",
            "Epoch 91: val_loss did not improve from 0.08594\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9753 - loss: 0.0704 - val_accuracy: 0.9700 - val_loss: 0.0999\n",
            "Epoch 92/100\n",
            "\u001b[1m35/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9675 - loss: 0.0903  \n",
            "Epoch 92: val_loss did not improve from 0.08594\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9696 - loss: 0.0861 - val_accuracy: 0.9600 - val_loss: 0.0954\n",
            "Epoch 93/100\n",
            "\u001b[1m33/50\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9662 - loss: 0.0829  \n",
            "Epoch 93: val_loss did not improve from 0.08594\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9678 - loss: 0.0824 - val_accuracy: 0.9450 - val_loss: 0.1186\n",
            "Epoch 94/100\n",
            "\u001b[1m27/50\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9782 - loss: 0.0570 \n",
            "Epoch 94: val_loss did not improve from 0.08594\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9772 - loss: 0.0648 - val_accuracy: 0.9700 - val_loss: 0.0934\n",
            "Epoch 95/100\n",
            "\u001b[1m31/50\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9740 - loss: 0.0686  \n",
            "Epoch 95: val_loss did not improve from 0.08594\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9748 - loss: 0.0688 - val_accuracy: 0.9400 - val_loss: 0.1284\n",
            "Epoch 96/100\n",
            "\u001b[1m27/50\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9833 - loss: 0.0697 \n",
            "Epoch 96: val_loss improved from 0.08594 to 0.08465, 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: 0.9803 - loss: 0.0722 - val_accuracy: 0.9750 - val_loss: 0.0847\n",
            "Epoch 97/100\n",
            "\u001b[1m45/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9802 - loss: 0.0666\n",
            "Epoch 97: val_loss did not improve from 0.08465\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9795 - loss: 0.0675 - val_accuracy: 0.9750 - val_loss: 0.0907\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: 0.9871 - loss: 0.0671 \n",
            "Epoch 98: val_loss improved from 0.08465 to 0.07852, 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: 0.9823 - loss: 0.0729 - val_accuracy: 0.9850 - val_loss: 0.0785\n",
            "Epoch 99/100\n",
            "\u001b[1m37/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9837 - loss: 0.0617  \n",
            "Epoch 99: val_loss did not improve from 0.07852\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9824 - loss: 0.0636 - val_accuracy: 0.9400 - val_loss: 0.1206\n",
            "Epoch 100/100\n",
            "\u001b[1m35/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9778 - loss: 0.0692 \n",
            "Epoch 100: val_loss did not improve from 0.07852\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9762 - loss: 0.0717 - val_accuracy: 0.9500 - val_loss: 0.1001\n",
            "Best validation loss: 0.0785176232457161\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": "7c6d8ded-f0d1-4aa2-8d6e-71f17b4510fb"
      },
      "execution_count": null,
      "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": "746eb3ac-962d-4fc6-ff5a-f556f17267f8"
      },
      "execution_count": null,
      "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": "bccfe05b-3c4d-4c59-ed33-5b86feb90a8f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step\n",
            "Accuracy:  0.985\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": "8cb17bd1-689c-4e37-8622-8c403b51a819"
      },
      "execution_count": null,
      "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": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = [30, 50, 70, 100]\n",
        "y = [0.965, 0.955, 0.945, 0.985]\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 Epoch', fontsize=14)\n",
        "plt.xlabel('Epoch', 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.savefig(f'/content/drive/My Drive/Colab Notebooks/accuracy_accros_epochs.png')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "c8zC90102o_p",
        "outputId": "b0cebccb-a296-48a9-9764-a8b1abc4ba74"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "drive.mount('/content/drive')\n",
        "loaded_model = load_model('/content/drive/My Drive/Colab Notebooks/stress-predictor-model-epoch-100.keras')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1QFrsfBXK8xP",
        "outputId": "24ea778b-c8e4-4e91-92da-503902e4f5e1"
      },
      "execution_count": null,
      "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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = np.array([[8.9, 0.3, 6.8, 0.7, 7.3, 3.4]])\n",
        "x_result = loaded_model.predict(x)\n",
        "np.argmax(x_result)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uM1Yy9WgLsY4",
        "outputId": "4df5e3f1-b152-4037-d0a4-186fc181e7d7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    }
  ]
}