SQLID.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5GJlrtW0R_HE",
        "outputId": "e8e3fb7b-2481-4255-b28a-c92d60128065"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Unique values in y_train: [0 1]\n",
            "Unique values in y_test: [0 1]\n",
            "Shape of X_train_pad: (54559, 227, 1)\n",
            "Shape of y_train: (54555,)\n",
            "Shape of X_test_pad: (13640, 227, 1)\n",
            "Shape of y_test: (13639,)\n",
            "Shape of X_train_pad: (13639, 227, 1)\n",
            "Shape of y_train: (13639,)\n",
            "Shape of X_test_pad: (13639, 227, 1)\n",
            "Shape of y_test: (13639,)\n",
            "Epoch 1/10\n",
            "427/427 [==============================] - 60s 138ms/step - loss: 0.1667 - accuracy: 0.9298 - val_loss: 2.5901 - val_accuracy: 0.6718\n",
            "Epoch 2/10\n",
            "427/427 [==============================] - 59s 139ms/step - loss: 0.0218 - accuracy: 0.9934 - val_loss: 3.4492 - val_accuracy: 0.6736\n",
            "Epoch 3/10\n",
            "427/427 [==============================] - 65s 152ms/step - loss: 0.0044 - accuracy: 0.9988 - val_loss: 4.0151 - val_accuracy: 0.6734\n",
            "Epoch 4/10\n",
            "427/427 [==============================] - 59s 138ms/step - loss: 0.0017 - accuracy: 0.9998 - val_loss: 4.2433 - val_accuracy: 0.6734\n",
            "Epoch 5/10\n",
            "427/427 [==============================] - 60s 140ms/step - loss: 9.4806e-04 - accuracy: 0.9998 - val_loss: 4.3886 - val_accuracy: 0.6729\n",
            "Epoch 6/10\n",
            "427/427 [==============================] - 59s 139ms/step - loss: 7.2621e-04 - accuracy: 0.9998 - val_loss: 4.5903 - val_accuracy: 0.6739\n",
            "Epoch 7/10\n",
            "427/427 [==============================] - 58s 136ms/step - loss: 5.5679e-04 - accuracy: 0.9999 - val_loss: 4.8037 - val_accuracy: 0.6740\n",
            "Epoch 8/10\n",
            "427/427 [==============================] - 59s 139ms/step - loss: 7.4449e-04 - accuracy: 0.9998 - val_loss: 4.8219 - val_accuracy: 0.6742\n",
            "Epoch 9/10\n",
            "427/427 [==============================] - 58s 136ms/step - loss: 4.7023e-04 - accuracy: 0.9999 - val_loss: 4.9536 - val_accuracy: 0.6739\n",
            "Epoch 10/10\n",
            "427/427 [==============================] - 59s 139ms/step - loss: 3.7965e-04 - accuracy: 0.9999 - val_loss: 5.2295 - val_accuracy: 0.6740\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7a58651bcd60>"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#1D CNN\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "from gensim.models import Word2Vec\n",
        "from keras.preprocessing.sequence import pad_sequences\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Embedding\n",
        "\n",
        "# Load the dataset\n",
        "data = pd.read_csv(\"mergecsv.csv\")\n",
        "\n",
        "# Pre-processing\n",
        "data['Input'] = data['Input'].apply(lambda x: x.lower().strip() if isinstance(x, str) else str(x))\n",
        "X_train, X_test, y_train, y_test = train_test_split(data['Input'], data['label'], test_size=0.2, random_state=42)\n",
        "\n",
        "# Word Embedding\n",
        "sentences = [sentence.split() for sentence in X_train]\n",
        "embedding_model = Word2Vec(sentences, vector_size=150, window=15, sg=1, epochs=15, min_count=1)  # Ensure all words are included\n",
        "word_vectors = embedding_model.wv\n",
        "vocab_size = len(word_vectors)\n",
        "\n",
        "# Convert words to indices, handling out-of-vocabulary words\n",
        "def get_word_index(word):\n",
        "    try:\n",
        "        return word_vectors.key_to_index[word]\n",
        "    except KeyError:\n",
        "        return 0  # Use index 0 for out-of-vocabulary words\n",
        "\n",
        "X_train_indices = [[get_word_index(word) for word in sentence.split()] for sentence in X_train]\n",
        "X_test_indices = [[get_word_index(word) for word in sentence.split()] for sentence in X_test]\n",
        "\n",
        "# Pad sequences to ensure uniform length\n",
        "max_length = 227\n",
        "X_train_pad = pad_sequences(X_train_indices, maxlen=max_length, padding='post')\n",
        "X_test_pad = pad_sequences(X_test_indices, maxlen=max_length, padding='post')\n",
        "\n",
        "# Reshape input data to add channel dimension\n",
        "X_train_pad = np.expand_dims(X_train_pad, axis=-1)\n",
        "X_test_pad = np.expand_dims(X_test_pad, axis=-1)\n",
        "\n",
        "from keras.layers import Conv1D, MaxPooling1D  # Import Conv1D\n",
        "\n",
        "# Build 1D CNN model\n",
        "model = Sequential()\n",
        "model.add(Embedding(input_dim=vocab_size, output_dim=150, input_length=max_length))\n",
        "model.add(Conv1D(filters=32, kernel_size=3, activation='relu'))  # Use Conv1D instead of Conv2D\n",
        "model.add(MaxPooling1D(pool_size=2))  # Use MaxPooling1D instead of MaxPooling2D\n",
        "model.add(Flatten())\n",
        "model.add(Dense(1, activation='sigmoid'))\n",
        "\n",
        "# Filter out unexpected string values from y_train and y_test\n",
        "y_train_filtered = y_train[~y_train.str.contains(r'\\D', na=False)]\n",
        "y_test_filtered = y_test[~y_test.str.contains(r'\\D', na=False)]\n",
        "\n",
        "# Convert labels to integers\n",
        "y_train = y_train_filtered.astype(int)\n",
        "y_test = y_test_filtered.astype(int)\n",
        "\n",
        "# Check the unique values again\n",
        "print(\"Unique values in y_train:\", np.unique(y_train))\n",
        "print(\"Unique values in y_test:\", np.unique(y_test))\n",
        "\n",
        "# Check the shapes of the input data and labels\n",
        "print(\"Shape of X_train_pad:\", X_train_pad.shape)\n",
        "print(\"Shape of y_train:\", y_train.shape)\n",
        "print(\"Shape of X_test_pad:\", X_test_pad.shape)\n",
        "print(\"Shape of y_test:\", y_test.shape)\n",
        "\n",
        "# Ensure that the input data and labels have the same number of samples\n",
        "min_samples = min(len(X_train_pad), len(y_train), len(X_test_pad), len(y_test))\n",
        "X_train_pad = X_train_pad[:min_samples]\n",
        "y_train = y_train[:min_samples]\n",
        "X_test_pad = X_test_pad[:min_samples]\n",
        "y_test = y_test[:min_samples]\n",
        "\n",
        "# Check the shapes again to confirm that they match\n",
        "print(\"Shape of X_train_pad:\", X_train_pad.shape)\n",
        "print(\"Shape of y_train:\", y_train.shape)\n",
        "print(\"Shape of X_test_pad:\", X_test_pad.shape)\n",
        "print(\"Shape of y_test:\", y_test.shape)\n",
        "\n",
        "# Compile and train the model\n",
        "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
        "model.fit(X_train_pad, y_train, epochs=10, batch_size=32, validation_data=(X_test_pad, y_test))\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 886
        },
        "id": "KDc2HC8mrtPO",
        "outputId": "68b15599-b969-4f3d-ff7e-46445ee83e25"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "1705/1705 [==============================] - 226s 131ms/step - loss: 0.7031 - accuracy: 0.7777 - val_loss: 0.3999 - val_accuracy: 0.8162\n",
            "Epoch 2/10\n",
            "1705/1705 [==============================] - 237s 139ms/step - loss: 0.3978 - accuracy: 0.8429 - val_loss: 0.3904 - val_accuracy: 0.8456\n",
            "Epoch 3/10\n",
            "1705/1705 [==============================] - 230s 135ms/step - loss: 0.3460 - accuracy: 0.8642 - val_loss: 0.3108 - val_accuracy: 0.8802\n",
            "Epoch 4/10\n",
            "1705/1705 [==============================] - 213s 125ms/step - loss: 0.3233 - accuracy: 0.8781 - val_loss: 0.2961 - val_accuracy: 0.8870\n",
            "Epoch 5/10\n",
            "1705/1705 [==============================] - 207s 121ms/step - loss: 0.3000 - accuracy: 0.8920 - val_loss: 0.2943 - val_accuracy: 0.9023\n",
            "Epoch 6/10\n",
            "1705/1705 [==============================] - 215s 126ms/step - loss: 0.2999 - accuracy: 0.8982 - val_loss: 0.2687 - val_accuracy: 0.9009\n",
            "Epoch 7/10\n",
            "1705/1705 [==============================] - 212s 124ms/step - loss: 0.2356 - accuracy: 0.9248 - val_loss: 0.2144 - val_accuracy: 0.9340\n",
            "Epoch 8/10\n",
            "1705/1705 [==============================] - 214s 126ms/step - loss: 0.2212 - accuracy: 0.9337 - val_loss: 0.2462 - val_accuracy: 0.9306\n",
            "Epoch 9/10\n",
            "1705/1705 [==============================] - 205s 120ms/step - loss: 0.1869 - accuracy: 0.9423 - val_loss: 0.1985 - val_accuracy: 0.9487\n",
            "Epoch 10/10\n",
            "1705/1705 [==============================] - 204s 120ms/step - loss: 0.1702 - accuracy: 0.9490 - val_loss: 0.1910 - val_accuracy: 0.9486\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x500 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "427/427 [==============================] - 14s 33ms/step - loss: 0.1910 - accuracy: 0.9486\n",
            "Test Loss: 0.1909940391778946\n",
            "Test Accuracy: 0.9486032724380493\n"
          ]
        }
      ],
      "source": [
        "#2D CNN with 3 convolutional,3 maxpooling layer, 1 flatten and 3 fully connected layer according to the reserach paper\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from keras.preprocessing.text import Tokenizer\n",
        "from keras.preprocessing.sequence import pad_sequences\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense\n",
        "\n",
        "# Step 1: Data Preprocessing\n",
        "data = pd.read_csv('mergecsv.csv')\n",
        "\n",
        "# Handle corner cases in the 'label' column\n",
        "def convert_to_int(label):\n",
        "    try:\n",
        "        return int(label)\n",
        "    except ValueError:\n",
        "        return -1  # Handle unexpected values by assigning a default label\n",
        "\n",
        "data['Input'] = data['Input'].apply(lambda x: str(x).lower().strip())\n",
        "data['label'] = data['label'].apply(convert_to_int)\n",
        "data = data[data['label'] != -1]  # Remove rows with unexpected values\n",
        "X = data['Input']\n",
        "y = data['label']\n",
        "\n",
        "# Step 2: Tokenization and Padding\n",
        "tokenizer = Tokenizer()\n",
        "tokenizer.fit_on_texts(X)\n",
        "X_indices = tokenizer.texts_to_sequences(X)\n",
        "X_pad = pad_sequences(X_indices)\n",
        "\n",
        "# Step 3: Reshaping for 2D CNN\n",
        "max_length = X_pad.shape[1]\n",
        "vocab_size = len(tokenizer.word_index) + 1\n",
        "X_pad = X_pad.reshape(X_pad.shape[0], max_length, 1, 1)  # Reshape for Conv2D input\n",
        "\n",
        "# Step 4: Building the 2D CNN Model\n",
        "model = Sequential()\n",
        "model.add(Conv2D(filters=64, kernel_size=(3, 1), activation='relu', input_shape=(max_length, 1, 1)))\n",
        "model.add(MaxPooling2D((2, 1)))  # Adjusted pool_size to (2, 1)\n",
        "model.add(Conv2D(filters=128, kernel_size=(3, 1), activation='relu'))\n",
        "model.add(MaxPooling2D((2, 1)))  # Adjusted pool_size to (2, 1)\n",
        "model.add(Conv2D(filters=256, kernel_size=(3, 1), activation='relu'))\n",
        "model.add(MaxPooling2D((2, 1)))  # Adjusted pool_size to (2, 1)\n",
        "model.add(Flatten())\n",
        "model.add(Dense(256, activation = \"relu\"))\n",
        "model.add(Dense(128, activation = \"relu\"))\n",
        "model.add(Dense(64, activation = \"relu\"))\n",
        "model.add(Dense(1, activation='sigmoid'))\n",
        "\n",
        "# Step 5: Compiling and Training the Model\n",
        "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_pad, y, test_size=0.2, random_state=42)\n",
        "model.summary()\n",
        "\n",
        "# Train the model and get history\n",
        "history = model.fit(X_train, y_train, epochs=10, batch_size=32, validation_data=(X_test, y_test))\n",
        "\n",
        "# Plot training history\n",
        "def plot_history(history):\n",
        "    plt.figure(figsize=(10, 5))\n",
        "    plt.subplot(1, 2, 1)\n",
        "    plt.plot(history.history['accuracy'], label='Train')\n",
        "    plt.plot(history.history['val_accuracy'], label='Validation')\n",
        "    plt.title('Model Accuracy')\n",
        "    plt.xlabel('Epoch')\n",
        "    plt.ylabel('Accuracy')\n",
        "    plt.legend()\n",
        "\n",
        "    plt.subplot(1, 2, 2)\n",
        "    plt.plot(history.history['loss'], label='Train')\n",
        "    plt.plot(history.history['val_loss'], label='Validation')\n",
        "    plt.title('Model Loss')\n",
        "    plt.xlabel('Epoch')\n",
        "    plt.ylabel('Loss')\n",
        "    plt.legend()\n",
        "    plt.show()\n",
        "\n",
        "plot_history(history)\n",
        "\n",
        "# Evaluate model on test data\n",
        "test_loss, test_accuracy = model.evaluate(X_test, y_test)\n",
        "print(\"Test Loss:\", test_loss)\n",
        "print(\"Test Accuracy:\", test_accuracy)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3O137aI5qzed",
        "outputId": "d7b65661-3391-4895-840e-e5f17d919106"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "1705/1705 [==============================] - 22s 12ms/step - loss: 14.1646 - accuracy: 0.8001 - val_loss: 4.3212 - val_accuracy: 0.8174\n",
            "Epoch 2/10\n",
            "1705/1705 [==============================] - 21s 12ms/step - loss: 2.7096 - accuracy: 0.8426 - val_loss: 2.0896 - val_accuracy: 0.8457\n",
            "Epoch 3/10\n",
            "1705/1705 [==============================] - 18s 11ms/step - loss: 1.5990 - accuracy: 0.8495 - val_loss: 0.9297 - val_accuracy: 0.8605\n",
            "Epoch 4/10\n",
            "1705/1705 [==============================] - 21s 12ms/step - loss: 0.9554 - accuracy: 0.8616 - val_loss: 0.9007 - val_accuracy: 0.8537\n",
            "Epoch 5/10\n",
            "1705/1705 [==============================] - 20s 11ms/step - loss: 0.4776 - accuracy: 0.8810 - val_loss: 0.2920 - val_accuracy: 0.8843\n",
            "Epoch 6/10\n",
            "1705/1705 [==============================] - 19s 11ms/step - loss: 0.3339 - accuracy: 0.8843 - val_loss: 0.2596 - val_accuracy: 0.8985\n",
            "Epoch 7/10\n",
            "1705/1705 [==============================] - 20s 11ms/step - loss: 0.3116 - accuracy: 0.8874 - val_loss: 0.3605 - val_accuracy: 0.8687\n",
            "Epoch 8/10\n",
            "1705/1705 [==============================] - 21s 12ms/step - loss: 0.3023 - accuracy: 0.8882 - val_loss: 0.3010 - val_accuracy: 0.8841\n",
            "Epoch 9/10\n",
            "1705/1705 [==============================] - 24s 14ms/step - loss: 0.2938 - accuracy: 0.8907 - val_loss: 0.2882 - val_accuracy: 0.8876\n",
            "Epoch 10/10\n",
            "1705/1705 [==============================] - 19s 11ms/step - loss: 0.2787 - accuracy: 0.8936 - val_loss: 0.2190 - val_accuracy: 0.9103\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7dcb28432ad0>"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#2D CNN simple(with single cnn and maxpooling layer)\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from keras.preprocessing.text import Tokenizer\n",
        "from keras.preprocessing.sequence import pad_sequences\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense\n",
        "\n",
        "# Step 1: Data Preprocessing\n",
        "data = pd.read_csv('mergecsv.csv')\n",
        "\n",
        "# Handle corner cases in the 'label' column\n",
        "def convert_to_int(label):\n",
        "    try:\n",
        "        return int(label)\n",
        "    except ValueError:\n",
        "        return -1  # Handle unexpected values by assigning a default label\n",
        "\n",
        "data['Input'] = data['Input'].apply(lambda x: str(x).lower().strip())\n",
        "data['label'] = data['label'].apply(convert_to_int)\n",
        "data = data[data['label'] != -1]  # Remove rows with unexpected values\n",
        "X = data['Input']\n",
        "y = data['label']\n",
        "\n",
        "# Step 2: Tokenization and Padding\n",
        "tokenizer = Tokenizer()\n",
        "tokenizer.fit_on_texts(X)\n",
        "X_indices = tokenizer.texts_to_sequences(X)\n",
        "X_pad = pad_sequences(X_indices)\n",
        "\n",
        "# Step 3: Reshaping for 2D CNN\n",
        "max_length = X_pad.shape[1]\n",
        "num_features = 1  # Since each word is represented by a single index\n",
        "X_pad = X_pad.reshape(X_pad.shape[0], max_length, num_features, 1)  # Reshape for Conv2D input\n",
        "\n",
        "# Step 4: Building the 2D CNN Model\n",
        "model = Sequential()\n",
        "model.add(Conv2D(filters=32, kernel_size=(3, 1), activation='relu', input_shape=(max_length, num_features, 1)))\n",
        "model.add(MaxPooling2D(pool_size=(2, 1)))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(1, activation='sigmoid'))\n",
        "\n",
        "# Step 5: Compiling and Training the Model\n",
        "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_pad, y, test_size=0.2, random_state=42)\n",
        "\n",
        "model.fit(X_train, y_train, epochs=10, batch_size=32, validation_data=(X_test, y_test))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 906
        },
        "id": "LioT3q0psXeV",
        "outputId": "a2497cd7-0723-4be9-ef82-d5d350af59f4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "1705/1705 [==============================] - 27s 16ms/step - loss: 0.2656 - accuracy: 0.8992 - val_loss: 0.2388 - val_accuracy: 0.8993\n",
            "Epoch 2/10\n",
            "1705/1705 [==============================] - 20s 12ms/step - loss: 0.2589 - accuracy: 0.9008 - val_loss: 0.2453 - val_accuracy: 0.9036\n",
            "Epoch 3/10\n",
            "1705/1705 [==============================] - 19s 11ms/step - loss: 0.2529 - accuracy: 0.9026 - val_loss: 0.2105 - val_accuracy: 0.9141\n",
            "Epoch 4/10\n",
            "1705/1705 [==============================] - 20s 12ms/step - loss: 0.2520 - accuracy: 0.9023 - val_loss: 0.2049 - val_accuracy: 0.9254\n",
            "Epoch 5/10\n",
            "1705/1705 [==============================] - 20s 12ms/step - loss: 0.2454 - accuracy: 0.9055 - val_loss: 0.2435 - val_accuracy: 0.8966\n",
            "Epoch 6/10\n",
            "1705/1705 [==============================] - 22s 13ms/step - loss: 0.2314 - accuracy: 0.9097 - val_loss: 0.2415 - val_accuracy: 0.9087\n",
            "Epoch 7/10\n",
            "1705/1705 [==============================] - 19s 11ms/step - loss: 0.2328 - accuracy: 0.9091 - val_loss: 0.2075 - val_accuracy: 0.9287\n",
            "Epoch 8/10\n",
            "1705/1705 [==============================] - 22s 13ms/step - loss: 0.2509 - accuracy: 0.9050 - val_loss: 0.2311 - val_accuracy: 0.9144\n",
            "Epoch 9/10\n",
            "1705/1705 [==============================] - 20s 12ms/step - loss: 0.2503 - accuracy: 0.9063 - val_loss: 0.2265 - val_accuracy: 0.9129\n",
            "Epoch 10/10\n",
            "1705/1705 [==============================] - 20s 12ms/step - loss: 0.2376 - accuracy: 0.9100 - val_loss: 0.2060 - val_accuracy: 0.9064\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x500 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "427/427 [==============================] - 2s 4ms/step - loss: 0.2060 - accuracy: 0.9064\n",
            "Test Loss: 0.20596684515476227\n",
            "Test Accuracy: 0.9064447283744812\n"
          ]
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Train the model\n",
        "history = model.fit(X_train, y_train, epochs=10, batch_size=32, validation_data=(X_test, y_test))\n",
        "\n",
        "# Plot training history\n",
        "def plot_history(history):\n",
        "    plt.figure(figsize=(10, 5))\n",
        "\n",
        "    # Plot training & validation accuracy values\n",
        "    plt.subplot(1, 2, 1)\n",
        "    plt.plot(history.history['accuracy'])\n",
        "    plt.plot(history.history['val_accuracy'])\n",
        "    plt.title('Model Accuracy')\n",
        "    plt.ylabel('Accuracy')\n",
        "    plt.xlabel('Epoch')\n",
        "    plt.legend(['Train', 'Validation'], loc='upper left')\n",
        "\n",
        "    # Plot training & validation loss values\n",
        "    plt.subplot(1, 2, 2)\n",
        "    plt.plot(history.history['loss'])\n",
        "    plt.plot(history.history['val_loss'])\n",
        "    plt.title('Model Loss')\n",
        "    plt.ylabel('Loss')\n",
        "    plt.xlabel('Epoch')\n",
        "    plt.legend(['Train', 'Validation'], loc='upper left')\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "\n",
        "plot_history(history)\n",
        "\n",
        "# Evaluate model on test data\n",
        "test_loss, test_accuracy = model.evaluate(X_test, y_test)\n",
        "print(\"Test Loss:\", test_loss)\n",
        "print(\"Test Accuracy:\", test_accuracy)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "plHk07F8tbSQ"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}