seasense-snn-ai-ml-accuracy.ipynb

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      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "0RfCfLhS9OdR",
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          "base_uri": "https://localhost:8080/"
        },
        "outputId": "350949f8-dfa4-4f7f-d4cd-45892c7f55a6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ],
      "source": [
        "!pip install tensorflow numpy pandas matplotlib seaborn scikit-learn"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Necessary Imports\n",
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.metrics import mean_squared_error, accuracy_score, classification_report\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.utils.class_weight import compute_class_weight\n",
        "from sklearn.preprocessing import MinMaxScaler, StandardScaler, LabelEncoder\n",
        "from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator\n",
        "# from tensorflow.keras.models import load_model\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, LSTM, GRU, Dropout\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.callbacks import EarlyStopping"
      ],
      "metadata": {
        "id": "w7feE7Mp9frS"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Coding Part"
      ],
      "metadata": {
        "id": "oInKAeNb92_1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def training_predict_fish_model(csv_file_path, epochs=10, batch_size=32):\n",
        "  df = pd.read_csv(csv_file_path)\n",
        "\n",
        "  print(df.columns)\n",
        "  print(\"Dataset Preview: \")\n",
        "  print(df.head())\n",
        "\n",
        "  # We encode species_name as integers\n",
        "  df['species_name_encoder'] = df['species_name'].astype('category').cat.codes\n",
        "  num_classes = df['species_name_encoder'].nunique()\n",
        "\n",
        "  # Extracting the day_of_year feature\n",
        "  df['date_catched'] = pd.to_datetime(df['date_catched'])\n",
        "  df['date_of_year'] = df['date_catched'].dt.dayofyear\n",
        "\n",
        "\n",
        "  # Features (X) and the Target (y)\n",
        "  X = df[['latitude', 'longitude', 'temperature', 'date_of_year']]\n",
        "  y = df['species_name_encoder']\n",
        "\n",
        "  # Scaling features\n",
        "  scaler = MinMaxScaler()\n",
        "  X_scaled = scaler.fit_transform(X)\n",
        "\n",
        "  # Splitting dataset for training and testing\n",
        "  X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)\n",
        "  y_train = y_train.to_numpy()\n",
        "\n",
        "  # Computing the class weights\n",
        "  class_weights = compute_class_weight('balanced', classes=np.unique(y_train), y=y_train)\n",
        "  class_weights_dict = dict(enumerate(class_weights))\n",
        "\n",
        "  # print(\"Unique classes in y_train:\", np.unique(y_train))\n",
        "  # print(\"Class weights dictionary:\", class_weights_dict)\n",
        "\n",
        "  model = Sequential([\n",
        "      Dense(64, activation='relu', input_dim=X_train.shape[1]),\n",
        "      Dense(32, activation='relu'),\n",
        "      Dense(num_classes, activation='softmax') #Multi-class output\n",
        "  ])\n",
        "\n",
        "  model.compile(optimizer=Adam(learning_rate=0.001),\n",
        "                loss='sparse_categorical_crossentropy',\n",
        "                metrics=['accuracy'])\n",
        "\n",
        "  print(set(y_train))  # Show the unique class labels in y_train\n",
        "  print(class_weights_dict.keys())\n",
        "\n",
        "  early_stopping = EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)\n",
        "\n",
        "  history = model.fit(X_train, y_train.astype(int),\n",
        "                      epochs=epochs,\n",
        "                      batch_size=batch_size,\n",
        "                      validation_data=(X_test,y_test),\n",
        "                      class_weight=class_weights_dict,\n",
        "                      callbacks=[early_stopping]\n",
        "                      )\n",
        "\n",
        "  # Evaluating the model\n",
        "  loss, accuracy = model.evaluate(X_test, y_test)\n",
        "  print(f'Loss: {loss}, Accuracy: {accuracy}')\n",
        "\n",
        "  # Assigning plot metrics\n",
        "  plt.figure(figsize=(10, 4))\n",
        "  plt.subplot(1, 2, 1)\n",
        "  plt.plot(history.history['accuracy'], label='Train accuracy')\n",
        "  plt.plot(history.history['val_accuracy'], label= 'Validation accuracy')\n",
        "  plt.title('Accuracy')\n",
        "  plt.legend()\n",
        "\n",
        "  plt.subplot(1, 2, 2)\n",
        "  plt.plot(history.history['loss'], label='Train loss')\n",
        "  plt.plot(history.history['val_loss'], label='Validation loss')\n",
        "  plt.title('Loss')\n",
        "  plt.legend()\n",
        "\n",
        "  return model, history\n",
        "\n",
        "csv_file_path = 'Sea_sense_d_data.csv'  # Replace with the actual path to your CSV file\n",
        "model, history = training_predict_fish_model(csv_file_path, epochs=100, batch_size=128)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "UADys0MB92IC",
        "outputId": "b2054989-25a5-42fc-ec36-df4587506987"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['fish_name', 'species_name', 'temperature', 'latitude', 'longitude',\n",
            "       'date_catched'],\n",
            "      dtype='object')\n",
            "Dataset Preview: \n",
            "       fish_name   species_name  temperature  latitude  longitude date_catched\n",
            "0  Bilong-bilong  Mene Maculata         27.2      6.91     125.58   11/12/2024\n",
            "1  Bilong-bilong  Mene Maculata         27.5      6.97     125.65   11/13/2024\n",
            "2  Bilong-bilong  Mene Maculata         27.1      6.86     125.70   11/14/2024\n",
            "3  Bilong-bilong  Mene Maculata         27.3      6.82     125.51   11/14/2024\n",
            "4  Bilong-bilong  Mene Maculata         27.6      6.75     125.48   11/15/2024\n",
            "{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21}\n",
            "dict_keys([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21])\n",
            "Epoch 1/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1s/step - accuracy: 0.0424 - loss: 3.1061 - val_accuracy: 0.0667 - val_loss: 3.1357\n",
            "Epoch 2/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - accuracy: 0.0508 - loss: 3.1016 - val_accuracy: 0.0667 - val_loss: 3.1313\n",
            "Epoch 3/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - accuracy: 0.0593 - loss: 3.0974 - val_accuracy: 0.0667 - val_loss: 3.1274\n",
            "Epoch 4/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - accuracy: 0.0678 - loss: 3.0935 - val_accuracy: 0.0667 - val_loss: 3.1235\n",
            "Epoch 5/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - accuracy: 0.0678 - loss: 3.0900 - val_accuracy: 0.0333 - val_loss: 3.1197\n",
            "Epoch 6/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - accuracy: 0.0593 - loss: 3.0868 - val_accuracy: 0.0333 - val_loss: 3.1163\n",
            "Epoch 7/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - accuracy: 0.0593 - loss: 3.0837 - val_accuracy: 0.0333 - val_loss: 3.1128\n",
            "Epoch 8/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - accuracy: 0.0593 - loss: 3.0808 - val_accuracy: 0.0333 - val_loss: 3.1093\n",
            "Epoch 9/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - accuracy: 0.0593 - loss: 3.0779 - val_accuracy: 0.0333 - val_loss: 3.1060\n",
            "Epoch 10/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - accuracy: 0.0508 - loss: 3.0750 - val_accuracy: 0.0333 - val_loss: 3.1028\n",
            "Epoch 11/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - accuracy: 0.0508 - loss: 3.0722 - val_accuracy: 0.0333 - val_loss: 3.0998\n",
            "Epoch 12/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - accuracy: 0.0508 - loss: 3.0695 - val_accuracy: 0.0333 - val_loss: 3.0969\n",
            "Epoch 13/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - accuracy: 0.0424 - loss: 3.0668 - val_accuracy: 0.0333 - val_loss: 3.0941\n",
            "Epoch 14/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 132ms/step - accuracy: 0.0424 - loss: 3.0641 - val_accuracy: 0.0333 - val_loss: 3.0913\n",
            "Epoch 15/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - accuracy: 0.0508 - loss: 3.0614 - val_accuracy: 0.0667 - val_loss: 3.0885\n",
            "Epoch 16/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - accuracy: 0.0508 - loss: 3.0589 - val_accuracy: 0.0667 - val_loss: 3.0860\n",
            "Epoch 17/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 120ms/step - accuracy: 0.0678 - loss: 3.0563 - val_accuracy: 0.0667 - val_loss: 3.0837\n",
            "Epoch 18/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - accuracy: 0.0932 - loss: 3.0538 - val_accuracy: 0.0333 - val_loss: 3.0815\n",
            "Epoch 19/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - accuracy: 0.0847 - loss: 3.0514 - val_accuracy: 0.0333 - val_loss: 3.0794\n",
            "Epoch 20/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step - accuracy: 0.0678 - loss: 3.0489 - val_accuracy: 0.0000e+00 - val_loss: 3.0773\n",
            "Epoch 21/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - accuracy: 0.0678 - loss: 3.0464 - val_accuracy: 0.0333 - val_loss: 3.0751\n",
            "Epoch 22/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step - accuracy: 0.0678 - loss: 3.0438 - val_accuracy: 0.0333 - val_loss: 3.0729\n",
            "Epoch 23/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - accuracy: 0.0763 - loss: 3.0411 - val_accuracy: 0.1000 - val_loss: 3.0705\n",
            "Epoch 24/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - accuracy: 0.0763 - loss: 3.0385 - val_accuracy: 0.1000 - val_loss: 3.0682\n",
            "Epoch 25/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - accuracy: 0.0847 - loss: 3.0359 - val_accuracy: 0.1333 - val_loss: 3.0657\n",
            "Epoch 26/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - accuracy: 0.0847 - loss: 3.0332 - val_accuracy: 0.1333 - val_loss: 3.0632\n",
            "Epoch 27/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - accuracy: 0.0763 - loss: 3.0305 - val_accuracy: 0.1333 - val_loss: 3.0605\n",
            "Epoch 28/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 150ms/step - accuracy: 0.0763 - loss: 3.0277 - val_accuracy: 0.1333 - val_loss: 3.0579\n",
            "Epoch 29/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - accuracy: 0.0763 - loss: 3.0249 - val_accuracy: 0.1333 - val_loss: 3.0553\n",
            "Epoch 30/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - accuracy: 0.0763 - loss: 3.0219 - val_accuracy: 0.1333 - val_loss: 3.0527\n",
            "Epoch 31/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - accuracy: 0.0932 - loss: 3.0188 - val_accuracy: 0.1333 - val_loss: 3.0501\n",
            "Epoch 32/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - accuracy: 0.0932 - loss: 3.0156 - val_accuracy: 0.1333 - val_loss: 3.0474\n",
            "Epoch 33/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - accuracy: 0.0932 - loss: 3.0124 - val_accuracy: 0.1333 - val_loss: 3.0446\n",
            "Epoch 34/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - accuracy: 0.1102 - loss: 3.0091 - val_accuracy: 0.1333 - val_loss: 3.0418\n",
            "Epoch 35/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - accuracy: 0.1271 - loss: 3.0059 - val_accuracy: 0.1333 - val_loss: 3.0390\n",
            "Epoch 36/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - accuracy: 0.1271 - loss: 3.0025 - val_accuracy: 0.1333 - val_loss: 3.0361\n",
            "Epoch 37/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 119ms/step - accuracy: 0.1186 - loss: 2.9991 - val_accuracy: 0.1333 - val_loss: 3.0330\n",
            "Epoch 38/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - accuracy: 0.1186 - loss: 2.9957 - val_accuracy: 0.1333 - val_loss: 3.0298\n",
            "Epoch 39/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - accuracy: 0.1186 - loss: 2.9923 - val_accuracy: 0.1333 - val_loss: 3.0267\n",
            "Epoch 40/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - accuracy: 0.1186 - loss: 2.9887 - val_accuracy: 0.1333 - val_loss: 3.0235\n",
            "Epoch 41/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - accuracy: 0.1186 - loss: 2.9853 - val_accuracy: 0.1333 - val_loss: 3.0203\n",
            "Epoch 42/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - accuracy: 0.1271 - loss: 2.9817 - val_accuracy: 0.1333 - val_loss: 3.0170\n",
            "Epoch 43/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - accuracy: 0.1271 - loss: 2.9781 - val_accuracy: 0.1333 - val_loss: 3.0137\n",
            "Epoch 44/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - accuracy: 0.1271 - loss: 2.9744 - val_accuracy: 0.1333 - val_loss: 3.0104\n",
            "Epoch 45/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - accuracy: 0.1271 - loss: 2.9707 - val_accuracy: 0.1333 - val_loss: 3.0071\n",
            "Epoch 46/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 156ms/step - accuracy: 0.1271 - loss: 2.9669 - val_accuracy: 0.1333 - val_loss: 3.0038\n",
            "Epoch 47/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 151ms/step - accuracy: 0.1271 - loss: 2.9630 - val_accuracy: 0.1333 - val_loss: 3.0006\n",
            "Epoch 48/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - accuracy: 0.1271 - loss: 2.9591 - val_accuracy: 0.1333 - val_loss: 2.9972\n",
            "Epoch 49/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 151ms/step - accuracy: 0.1271 - loss: 2.9551 - val_accuracy: 0.1333 - val_loss: 2.9940\n",
            "Epoch 50/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - accuracy: 0.1186 - loss: 2.9510 - val_accuracy: 0.1333 - val_loss: 2.9907\n",
            "Epoch 51/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - accuracy: 0.1186 - loss: 2.9469 - val_accuracy: 0.1333 - val_loss: 2.9874\n",
            "Epoch 52/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - accuracy: 0.1186 - loss: 2.9426 - val_accuracy: 0.1333 - val_loss: 2.9840\n",
            "Epoch 53/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - accuracy: 0.1186 - loss: 2.9383 - val_accuracy: 0.1333 - val_loss: 2.9807\n",
            "Epoch 54/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - accuracy: 0.1186 - loss: 2.9338 - val_accuracy: 0.1333 - val_loss: 2.9774\n",
            "Epoch 55/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - accuracy: 0.1271 - loss: 2.9292 - val_accuracy: 0.1333 - val_loss: 2.9741\n",
            "Epoch 56/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - accuracy: 0.1271 - loss: 2.9246 - val_accuracy: 0.1333 - val_loss: 2.9706\n",
            "Epoch 57/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - accuracy: 0.1271 - loss: 2.9198 - val_accuracy: 0.1333 - val_loss: 2.9671\n",
            "Epoch 58/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - accuracy: 0.1356 - loss: 2.9149 - val_accuracy: 0.1333 - val_loss: 2.9635\n",
            "Epoch 59/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - accuracy: 0.1356 - loss: 2.9099 - val_accuracy: 0.1333 - val_loss: 2.9597\n",
            "Epoch 60/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - accuracy: 0.1441 - loss: 2.9048 - val_accuracy: 0.1333 - val_loss: 2.9558\n",
            "Epoch 61/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - accuracy: 0.1525 - loss: 2.8996 - val_accuracy: 0.1333 - val_loss: 2.9518\n",
            "Epoch 62/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - accuracy: 0.1610 - loss: 2.8943 - val_accuracy: 0.1333 - val_loss: 2.9477\n",
            "Epoch 63/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - accuracy: 0.1525 - loss: 2.8889 - val_accuracy: 0.1333 - val_loss: 2.9436\n",
            "Epoch 64/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - accuracy: 0.1610 - loss: 2.8834 - val_accuracy: 0.1333 - val_loss: 2.9395\n",
            "Epoch 65/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - accuracy: 0.1610 - loss: 2.8778 - val_accuracy: 0.1333 - val_loss: 2.9353\n",
            "Epoch 66/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - accuracy: 0.1610 - loss: 2.8720 - val_accuracy: 0.1333 - val_loss: 2.9311\n",
            "Epoch 67/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - accuracy: 0.1610 - loss: 2.8662 - val_accuracy: 0.1333 - val_loss: 2.9268\n",
            "Epoch 68/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - accuracy: 0.1610 - loss: 2.8602 - val_accuracy: 0.1333 - val_loss: 2.9226\n",
            "Epoch 69/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - accuracy: 0.1695 - loss: 2.8542 - val_accuracy: 0.1333 - val_loss: 2.9185\n",
            "Epoch 70/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - accuracy: 0.1780 - loss: 2.8481 - val_accuracy: 0.1333 - val_loss: 2.9142\n",
            "Epoch 71/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - accuracy: 0.1949 - loss: 2.8418 - val_accuracy: 0.1333 - val_loss: 2.9099\n",
            "Epoch 72/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - accuracy: 0.1949 - loss: 2.8353 - val_accuracy: 0.1333 - val_loss: 2.9054\n",
            "Epoch 73/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - accuracy: 0.1949 - loss: 2.8286 - val_accuracy: 0.1333 - val_loss: 2.9006\n",
            "Epoch 74/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - accuracy: 0.1949 - loss: 2.8217 - val_accuracy: 0.1333 - val_loss: 2.8957\n",
            "Epoch 75/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - accuracy: 0.1949 - loss: 2.8148 - val_accuracy: 0.1333 - val_loss: 2.8906\n",
            "Epoch 76/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - accuracy: 0.2034 - loss: 2.8077 - val_accuracy: 0.1333 - val_loss: 2.8854\n",
            "Epoch 77/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 152ms/step - accuracy: 0.2119 - loss: 2.8004 - val_accuracy: 0.1333 - val_loss: 2.8800\n",
            "Epoch 78/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - accuracy: 0.2119 - loss: 2.7930 - val_accuracy: 0.1333 - val_loss: 2.8747\n",
            "Epoch 79/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - accuracy: 0.2203 - loss: 2.7854 - val_accuracy: 0.1333 - val_loss: 2.8695\n",
            "Epoch 80/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - accuracy: 0.2119 - loss: 2.7778 - val_accuracy: 0.1333 - val_loss: 2.8641\n",
            "Epoch 81/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - accuracy: 0.2119 - loss: 2.7700 - val_accuracy: 0.1333 - val_loss: 2.8585\n",
            "Epoch 82/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - accuracy: 0.2119 - loss: 2.7620 - val_accuracy: 0.1333 - val_loss: 2.8528\n",
            "Epoch 83/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - accuracy: 0.2203 - loss: 2.7539 - val_accuracy: 0.1333 - val_loss: 2.8470\n",
            "Epoch 84/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 172ms/step - accuracy: 0.2203 - loss: 2.7457 - val_accuracy: 0.1333 - val_loss: 2.8410\n",
            "Epoch 85/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 169ms/step - accuracy: 0.2203 - loss: 2.7373 - val_accuracy: 0.1333 - val_loss: 2.8349\n",
            "Epoch 86/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - accuracy: 0.2203 - loss: 2.7288 - val_accuracy: 0.1333 - val_loss: 2.8287\n",
            "Epoch 87/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 119ms/step - accuracy: 0.2288 - loss: 2.7202 - val_accuracy: 0.1333 - val_loss: 2.8224\n",
            "Epoch 88/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step - accuracy: 0.2288 - loss: 2.7113 - val_accuracy: 0.1333 - val_loss: 2.8160\n",
            "Epoch 89/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - accuracy: 0.2288 - loss: 2.7023 - val_accuracy: 0.1667 - val_loss: 2.8095\n",
            "Epoch 90/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 330ms/step - accuracy: 0.2203 - loss: 2.6932 - val_accuracy: 0.1667 - val_loss: 2.8029\n",
            "Epoch 91/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129ms/step - accuracy: 0.2203 - loss: 2.6840 - val_accuracy: 0.1667 - val_loss: 2.7962\n",
            "Epoch 92/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - accuracy: 0.2288 - loss: 2.6746 - val_accuracy: 0.1667 - val_loss: 2.7893\n",
            "Epoch 93/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 152ms/step - accuracy: 0.2288 - loss: 2.6650 - val_accuracy: 0.1667 - val_loss: 2.7824\n",
            "Epoch 94/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - accuracy: 0.2373 - loss: 2.6553 - val_accuracy: 0.1667 - val_loss: 2.7754\n",
            "Epoch 95/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 120ms/step - accuracy: 0.2373 - loss: 2.6456 - val_accuracy: 0.1667 - val_loss: 2.7682\n",
            "Epoch 96/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129ms/step - accuracy: 0.2373 - loss: 2.6358 - val_accuracy: 0.1667 - val_loss: 2.7608\n",
            "Epoch 97/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 310ms/step - accuracy: 0.2373 - loss: 2.6259 - val_accuracy: 0.1667 - val_loss: 2.7532\n",
            "Epoch 98/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - accuracy: 0.2373 - loss: 2.6158 - val_accuracy: 0.1667 - val_loss: 2.7455\n",
            "Epoch 99/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - accuracy: 0.2373 - loss: 2.6056 - val_accuracy: 0.1667 - val_loss: 2.7376\n",
            "Epoch 100/100\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - accuracy: 0.2458 - loss: 2.5952 - val_accuracy: 0.1667 - val_loss: 2.7296\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - accuracy: 0.1667 - loss: 2.7296\n",
            "Loss: 2.7296299934387207, Accuracy: 0.1666666716337204\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "  # print(\"Class weights dictionary passed to fit():\", class_weights_dict)\n",
        "  # print(\"Data type of keys in class_weights_dict:\", [type(k) for k in class_weights_dict.keys()])\n",
        "  # print(\"Data type of labels in y_train:\", type(y_train.iloc[0]))\n",
        "\n",
        "    # # Check the data type of y_train\n",
        "  # print(f\"Data type of y_train: {type(y_train)}\")\n",
        "  # print(f\"Shape of y_train: {y_train.shape}\")\n",
        "\n",
        "  # # Check the data type of class_weights_dict\n",
        "  # print(f\"Data type of class_weights_dict: {type(class_weights_dict)}\")\n",
        "  # print(f\"Content of class_weights_dict: {class_weights_dict}\")"
      ],
      "metadata": {
        "id": "6fRWyLA8ITdN"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}