HateSpeechDetect_using_DeepLearning.ipynb

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        "<a href=\"https://colab.research.google.com/github/devroopsaha744/HateSpeechDetect-text/blob/main/HateSpeechDetect_using_DeepLearning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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            "\u001b[?25h"
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      ],
      "source": [
        "!pip install datasets -q\n",
        "!pip install imbalanced-learn -q"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import classification_report\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import SimpleRNN, LSTM, Dense, Dropout, GRU, Bidirectional\n",
        "from tensorflow.keras.utils import pad_sequences, to_categorical\n",
        "from imblearn.over_sampling import SMOTE\n",
        "from imblearn.under_sampling import RandomUnderSampler\n",
        "from imblearn.pipeline import Pipeline\n",
        "from tensorflow.keras.models import load_model"
      ],
      "metadata": {
        "id": "Rpmhqf0X7HTr"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "nltk.download('punkt')\n",
        "nltk.download('stopwords')\n",
        "nltk.download('wordnet')\n",
        "from gensim.models import Word2Vec\n",
        "import re\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.tokenize import word_tokenize\n",
        "from nltk.stem import WordNetLemmatizer"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ldFlKSUY_eoO",
        "outputId": "067a647e-2de6-47b9-a102-29f184359906"
      },
      "execution_count": 3,
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        {
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          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n"
          ]
        }
      ]
    },
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        "data_split = data['train'].train_test_split(test_size = 0.3)\n",
        "train = data_split['train']\n",
        "test = data_split['test']"
      ],
      "metadata": {
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    {
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        "train_df = train.to_pandas()\n",
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        "# Load the stop words\n",
        "stop_words = set(stopwords.words('english'))\n",
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        "\n",
        "def preprocess_text(text):\n",
        "    # Lowercase the text\n",
        "    text = text.lower()\n",
        "    # Remove special characters and punctuation\n",
        "    text = re.sub(r'[^a-zA-Z0-9\\s]', '', text)\n",
        "    # Tokenize the text\n",
        "    tokens = word_tokenize(text)\n",
        "    # Remove stop words and lemmatize the tokens\n",
        "    tokens = [lemmatizer.lemmatize(word) for word in tokens if word not in stop_words]\n",
        "    # Join the tokens back into a single string\n",
        "    text = ' '.join(tokens)\n",
        "    return text\n",
        "\n",
        "# Apply the preprocessing function to the 'text' column\n",
        "train_df['tweet'] = train_df['tweet'].apply(preprocess_text)\n",
        "test_df['tweet'] = test_df['tweet'].apply(preprocess_text)"
      ],
      "metadata": {
        "id": "PC9DHpKX_U36"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_df['tokens'] = train_df['tweet'].apply(word_tokenize)\n",
        "test_df['tokens'] =  test_df['tweet'].apply(word_tokenize)\n",
        "\n",
        "# Train Word2Vec model\n",
        "model = Word2Vec(sentences=train_df['tokens'], vector_size=100, window=5, min_count=1, workers=4)\n",
        "def get_average_word2vec(tokens, model, vector_size):\n",
        "    # Initialize an empty vector\n",
        "    vec = np.zeros(vector_size)\n",
        "    count = 0\n",
        "    for word in tokens:\n",
        "        if word in model.wv:\n",
        "            vec += model.wv[word]\n",
        "            count += 1\n",
        "    if count != 0:\n",
        "        vec /= count\n",
        "    return vec\n",
        "\n",
        "vector_size = model.vector_size\n",
        "\n",
        "train_df['text_vec'] = train_df['tokens'].apply(lambda x: get_average_word2vec(x, model, vector_size))\n",
        "test_df['text_vec'] = test_df['tokens'].apply(lambda x: get_average_word2vec(x, model, vector_size))\n",
        "\n",
        "# Convert lists of vectors to a 2D array for the classifier\n",
        "X_train = np.stack(train_df['text_vec'].values)\n",
        "y_train = train_df['class'].values\n",
        "X_test = np.stack(test_df['text_vec'].values)\n",
        "y_test = test_df['class'].values"
      ],
      "metadata": {
        "id": "ODnOAP7W_ymQ"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Applying data-resampling\n",
        "# Define the resampling pipeline\n",
        "over = SMOTE(sampling_strategy= 'auto')  # Oversample the minority class to 10% of the majority class\n",
        "under = RandomUnderSampler(sampling_strategy='auto')  # Undersample the majority class to 50% of the original\n",
        "\n",
        "# Combine the oversampling and undersampling in a pipeline\n",
        "steps = [('o', over), ('u', under)]\n",
        "pipeline = Pipeline(steps=steps)\n",
        "\n",
        "# Apply resampling to the training data\n",
        "X_train_res, y_train_res = pipeline.fit_resample(X_train, y_train)"
      ],
      "metadata": {
        "id": "nfslKNASf0Gh"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_train_res = to_categorical(y_train_res, num_classes=3)\n",
        "y_test = to_categorical(y_test, num_classes=3)"
      ],
      "metadata": {
        "id": "NwkU9VE--lNF"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_train_res"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pzfjtXuG-obz",
        "outputId": "c477a164-5119-4192-e3b4-c070fbb29324"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1., 0., 0.],\n",
              "       [1., 0., 0.],\n",
              "       [1., 0., 0.],\n",
              "       ...,\n",
              "       [0., 0., 1.],\n",
              "       [0., 0., 1.],\n",
              "       [0., 0., 1.]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "\n",
        "model.add(LSTM(128,input_shape=(100,1),return_sequences=False))\n",
        "model.add(Dense(64, activation = 'relu'))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(Dense(3,activation='softmax'))\n",
        "\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H0xXKSPfCCBw",
        "outputId": "0a09d3de-7da8-4324-a109-7d24517b814b"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm (LSTM)                 (None, 128)               66560     \n",
            "                                                                 \n",
            " dense (Dense)               (None, 64)                8256      \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 64)                0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 3)                 195       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 75011 (293.01 KB)\n",
            "Trainable params: 75011 (293.01 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "6TtEakjtCcDG"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X_train_res,y_train_res,epochs=50,validation_data=(X_test,y_test), batch_size = 32)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IHLMJg4UCm0D",
        "outputId": "dc882461-8ea7-4de3-f7f0-0b9322ebb32b"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "1256/1256 [==============================] - 14s 8ms/step - loss: 1.0145 - accuracy: 0.4741 - val_loss: 0.9322 - val_accuracy: 0.7450\n",
            "Epoch 2/50\n",
            "1256/1256 [==============================] - 10s 8ms/step - loss: 1.0084 - accuracy: 0.4794 - val_loss: 0.9027 - val_accuracy: 0.7143\n",
            "Epoch 3/50\n",
            "1256/1256 [==============================] - 10s 8ms/step - loss: 1.0076 - accuracy: 0.4800 - val_loss: 0.9416 - val_accuracy: 0.6467\n",
            "Epoch 4/50\n",
            "1256/1256 [==============================] - 9s 7ms/step - loss: 1.0078 - accuracy: 0.4810 - val_loss: 0.9019 - val_accuracy: 0.7083\n",
            "Epoch 5/50\n",
            "1256/1256 [==============================] - 9s 7ms/step - loss: 1.0063 - accuracy: 0.4820 - val_loss: 0.9221 - val_accuracy: 0.7069\n",
            "Epoch 6/50\n",
            "1256/1256 [==============================] - 10s 8ms/step - loss: 1.0045 - accuracy: 0.4824 - val_loss: 0.8600 - val_accuracy: 0.7318\n",
            "Epoch 7/50\n",
            "1256/1256 [==============================] - 9s 7ms/step - loss: 0.9958 - accuracy: 0.4885 - val_loss: 0.9031 - val_accuracy: 0.6430\n",
            "Epoch 8/50\n",
            "1256/1256 [==============================] - 9s 7ms/step - loss: 0.9676 - accuracy: 0.5157 - val_loss: 0.8252 - val_accuracy: 0.7303\n",
            "Epoch 9/50\n",
            "1256/1256 [==============================] - 10s 8ms/step - loss: 0.9434 - accuracy: 0.5344 - val_loss: 0.7842 - val_accuracy: 0.7801\n",
            "Epoch 10/50\n",
            "1256/1256 [==============================] - 10s 8ms/step - loss: 1.0186 - accuracy: 0.4931 - val_loss: 0.9275 - val_accuracy: 0.6753\n",
            "Epoch 11/50\n",
            "1256/1256 [==============================] - 10s 8ms/step - loss: 0.9774 - accuracy: 0.5150 - val_loss: 0.8562 - val_accuracy: 0.7345\n",
            "Epoch 12/50\n",
            "1256/1256 [==============================] - 10s 8ms/step - loss: 1.0054 - accuracy: 0.5047 - val_loss: 0.8929 - val_accuracy: 0.7210\n",
            "Epoch 13/50\n",
            "1256/1256 [==============================] - 10s 8ms/step - loss: 0.9884 - accuracy: 0.4974 - val_loss: 0.8910 - val_accuracy: 0.7167\n",
            "Epoch 14/50\n",
            "1256/1256 [==============================] - 10s 8ms/step - loss: 0.9794 - accuracy: 0.5071 - val_loss: 0.9804 - val_accuracy: 0.5723\n",
            "Epoch 15/50\n",
            "1256/1256 [==============================] - 9s 7ms/step - loss: 0.9744 - accuracy: 0.5137 - val_loss: 0.8642 - val_accuracy: 0.6946\n",
            "Epoch 16/50\n",
            "1256/1256 [==============================] - 10s 8ms/step - loss: 0.9704 - accuracy: 0.5149 - val_loss: 0.7928 - val_accuracy: 0.7354\n",
            "Epoch 17/50\n",
            "1256/1256 [==============================] - 10s 8ms/step - loss: 0.9682 - accuracy: 0.5175 - val_loss: 0.7944 - val_accuracy: 0.7482\n",
            "Epoch 18/50\n",
            "1256/1256 [==============================] - 10s 8ms/step - loss: 0.9625 - accuracy: 0.5205 - val_loss: 0.7663 - val_accuracy: 0.7572\n",
            "Epoch 19/50\n",
            "1256/1256 [==============================] - 9s 7ms/step - loss: 0.9570 - accuracy: 0.5270 - val_loss: 0.8239 - val_accuracy: 0.7067\n",
            "Epoch 20/50\n",
            "1256/1256 [==============================] - 10s 8ms/step - loss: 0.9448 - accuracy: 0.5327 - val_loss: 0.7900 - val_accuracy: 0.7459\n",
            "Epoch 21/50\n",
            "1256/1256 [==============================] - 10s 8ms/step - loss: 0.9371 - accuracy: 0.5369 - val_loss: 0.9784 - val_accuracy: 0.6200\n",
            "Epoch 22/50\n",
            "1256/1256 [==============================] - 9s 7ms/step - loss: 0.9159 - accuracy: 0.5510 - val_loss: 0.8328 - val_accuracy: 0.7174\n",
            "Epoch 23/50\n",
            "1256/1256 [==============================] - 10s 8ms/step - loss: 0.8884 - accuracy: 0.5693 - val_loss: 0.8816 - val_accuracy: 0.7208\n",
            "Epoch 24/50\n",
            "1256/1256 [==============================] - 10s 8ms/step - loss: 0.8748 - accuracy: 0.5777 - val_loss: 0.7120 - val_accuracy: 0.7872\n",
            "Epoch 25/50\n",
            "1256/1256 [==============================] - 10s 8ms/step - loss: 0.8713 - accuracy: 0.5782 - val_loss: 0.7381 - val_accuracy: 0.7910\n",
            "Epoch 26/50\n",
            "1256/1256 [==============================] - 9s 7ms/step - loss: 0.8572 - accuracy: 0.5871 - val_loss: 0.8120 - val_accuracy: 0.7381\n",
            "Epoch 27/50\n",
            "1256/1256 [==============================] - 9s 8ms/step - loss: 0.8570 - accuracy: 0.5857 - val_loss: 0.7205 - val_accuracy: 0.7878\n",
            "Epoch 28/50\n",
            "1256/1256 [==============================] - 10s 8ms/step - loss: 0.8489 - accuracy: 0.5886 - val_loss: 0.7531 - val_accuracy: 0.7775\n",
            "Epoch 29/50\n",
            "1256/1256 [==============================] - 9s 7ms/step - loss: 0.8410 - accuracy: 0.5948 - val_loss: 0.7696 - val_accuracy: 0.7763\n",
            "Epoch 30/50\n",
            "1256/1256 [==============================] - 9s 7ms/step - loss: 0.8343 - accuracy: 0.6008 - val_loss: 0.6617 - val_accuracy: 0.7919\n",
            "Epoch 31/50\n",
            "1256/1256 [==============================] - 11s 8ms/step - loss: 0.8304 - accuracy: 0.6054 - val_loss: 0.6524 - val_accuracy: 0.8007\n",
            "Epoch 32/50\n",
            "1256/1256 [==============================] - 10s 8ms/step - loss: 0.8307 - accuracy: 0.6062 - val_loss: 0.7661 - val_accuracy: 0.8051\n",
            "Epoch 33/50\n",
            "1256/1256 [==============================] - 9s 7ms/step - loss: 0.8253 - accuracy: 0.6080 - val_loss: 0.7575 - val_accuracy: 0.7680\n",
            "Epoch 34/50\n",
            "1256/1256 [==============================] - 10s 8ms/step - loss: 0.8196 - accuracy: 0.6102 - val_loss: 0.7477 - val_accuracy: 0.7668\n",
            "Epoch 35/50\n",
            "1256/1256 [==============================] - 10s 8ms/step - loss: 0.8137 - accuracy: 0.6178 - val_loss: 0.7756 - val_accuracy: 0.7617\n",
            "Epoch 36/50\n",
            "1256/1256 [==============================] - 9s 7ms/step - loss: 0.8076 - accuracy: 0.6215 - val_loss: 0.6612 - val_accuracy: 0.7991\n",
            "Epoch 37/50\n",
            "1256/1256 [==============================] - 9s 7ms/step - loss: 0.8057 - accuracy: 0.6229 - val_loss: 0.6641 - val_accuracy: 0.8023\n",
            "Epoch 38/50\n",
            "1256/1256 [==============================] - 10s 8ms/step - loss: 0.8036 - accuracy: 0.6232 - val_loss: 0.6947 - val_accuracy: 0.7650\n",
            "Epoch 39/50\n",
            "1256/1256 [==============================] - 10s 8ms/step - loss: 0.7987 - accuracy: 0.6287 - val_loss: 0.6053 - val_accuracy: 0.7980\n",
            "Epoch 40/50\n",
            "1256/1256 [==============================] - 9s 7ms/step - loss: 0.7901 - accuracy: 0.6350 - val_loss: 0.6638 - val_accuracy: 0.7987\n",
            "Epoch 41/50\n",
            "1256/1256 [==============================] - 9s 7ms/step - loss: 0.7883 - accuracy: 0.6338 - val_loss: 0.7668 - val_accuracy: 0.7500\n",
            "Epoch 42/50\n",
            "1256/1256 [==============================] - 9s 7ms/step - loss: 0.7839 - accuracy: 0.6340 - val_loss: 0.7215 - val_accuracy: 0.7583\n",
            "Epoch 43/50\n",
            "1256/1256 [==============================] - 10s 8ms/step - loss: 0.7824 - accuracy: 0.6385 - val_loss: 0.6386 - val_accuracy: 0.7914\n",
            "Epoch 44/50\n",
            "1256/1256 [==============================] - 9s 7ms/step - loss: 0.7792 - accuracy: 0.6384 - val_loss: 0.7634 - val_accuracy: 0.7377\n",
            "Epoch 45/50\n",
            "1256/1256 [==============================] - 10s 8ms/step - loss: 0.7759 - accuracy: 0.6417 - val_loss: 0.6704 - val_accuracy: 0.7626\n",
            "Epoch 46/50\n",
            "1256/1256 [==============================] - 9s 7ms/step - loss: 0.7723 - accuracy: 0.6441 - val_loss: 0.6623 - val_accuracy: 0.7471\n",
            "Epoch 47/50\n",
            "1256/1256 [==============================] - 9s 7ms/step - loss: 0.7715 - accuracy: 0.6465 - val_loss: 0.6626 - val_accuracy: 0.7802\n",
            "Epoch 48/50\n",
            "1256/1256 [==============================] - 10s 8ms/step - loss: 0.7664 - accuracy: 0.6489 - val_loss: 0.6304 - val_accuracy: 0.7849\n",
            "Epoch 49/50\n",
            "1256/1256 [==============================] - 9s 8ms/step - loss: 0.7662 - accuracy: 0.6487 - val_loss: 0.6253 - val_accuracy: 0.7966\n",
            "Epoch 50/50\n",
            "1256/1256 [==============================] - 10s 8ms/step - loss: 0.7619 - accuracy: 0.6521 - val_loss: 0.7189 - val_accuracy: 0.7279\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7d88d846dbd0>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = model.predict(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a5khn0rrbJXl",
        "outputId": "b5538417-3a95-4098-8947-b1794b0c9776"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "233/233 [==============================] - 1s 3ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SrUTuYdzbQgN",
        "outputId": "54caeb70-41f2-4f53-e1c5-13544c7adeed"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.20659773, 0.77047944, 0.02292282],\n",
              "       [0.33701906, 0.08009719, 0.5828837 ],\n",
              "       [0.42827934, 0.389693  , 0.18202765],\n",
              "       ...,\n",
              "       [0.14547609, 0.03614113, 0.8183828 ],\n",
              "       [0.4858342 , 0.21205065, 0.3021151 ],\n",
              "       [0.45058474, 0.17845197, 0.37096334]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_classes = np.argmax(y_pred, axis = 1)"
      ],
      "metadata": {
        "id": "K24t4BQrbpNd"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "set(y_pred_classes)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P200dCJvbu7o",
        "outputId": "c8b5769f-2304-456f-aa99-b21227691866"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{0, 1, 2}"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test_classes = np.argmax(y_test, axis = 1)"
      ],
      "metadata": {
        "id": "kYizFH9vMrcm"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "set(y_test_classes)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ay7zTrdiMwOi",
        "outputId": "442a11bc-1b5b-46c1-ad90-5073998be71f"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{0, 1, 2}"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test_classes, y_pred_classes))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZBnoSRrxbxH2",
        "outputId": "60b6d1e4-14ca-4d2b-cfb8-eb5db6e46198"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.11      0.42      0.17       424\n",
            "           1       0.94      0.82      0.87      5796\n",
            "           2       0.68      0.39      0.49      1215\n",
            "\n",
            "    accuracy                           0.73      7435\n",
            "   macro avg       0.57      0.54      0.51      7435\n",
            "weighted avg       0.85      0.73      0.77      7435\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.save(\"LSTM-HateSpeech.h5\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uTLhce8Ve-zn",
        "outputId": "0555336a-1e3b-44da-85a3-95026e0e2792"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
            "  saving_api.save_model(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Metrics for LSTM\n",
        "'''\n",
        "       precision    recall  f1-score   support\n",
        "\n",
        "           0       0.11      0.42      0.17       424\n",
        "           1       0.94      0.82      0.87      5796\n",
        "           2       0.68      0.39      0.49      1215\n",
        "\n",
        "    accuracy                           0.73      7435\n",
        "   macro avg       0.57      0.54      0.51      7435\n",
        "weighted avg       0.85      0.73      0.77      7435\n",
        "\n",
        "'''"
      ],
      "metadata": {
        "id": "6LCBp0WVNBOG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **GRU**"
      ],
      "metadata": {
        "id": "6V_E7-q6Nje1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "gru = Sequential()\n",
        "\n",
        "gru.add(GRU(128,input_shape=(100,1),return_sequences=False))\n",
        "gru.add(Dense(64, activation = 'relu'))\n",
        "gru.add(Dropout(0.2))\n",
        "gru.add(Dense(3,activation='softmax'))\n",
        "\n",
        "gru.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5kmZczxGNgvm",
        "outputId": "95ed6b31-b02e-4ed7-b6c7-38f444ddd6a2"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " gru (GRU)                   (None, 128)               50304     \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 64)                8256      \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 64)                0         \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 3)                 195       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 58755 (229.51 KB)\n",
            "Trainable params: 58755 (229.51 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gru.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "cD0dI9RxNsvO"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "gru.fit(X_train_res,y_train_res,epochs=50,validation_data=(X_test,y_test), batch_size = 32)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0aRB1BE6N6QS",
        "outputId": "5dbc1dc7-a5da-4030-a64b-5078819450cd"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "1256/1256 [==============================] - 16s 8ms/step - loss: 1.0140 - accuracy: 0.4754 - val_loss: 0.8746 - val_accuracy: 0.7153\n",
            "Epoch 2/50\n",
            "1256/1256 [==============================] - 9s 7ms/step - loss: 1.0088 - accuracy: 0.4790 - val_loss: 0.9413 - val_accuracy: 0.6721\n",
            "Epoch 3/50\n",
            "1256/1256 [==============================] - 9s 7ms/step - loss: 1.0083 - accuracy: 0.4803 - val_loss: 0.9632 - val_accuracy: 0.6714\n",
            "Epoch 4/50\n",
            "1256/1256 [==============================] - 9s 7ms/step - loss: 1.0074 - accuracy: 0.4828 - val_loss: 0.9108 - val_accuracy: 0.6933\n",
            "Epoch 5/50\n",
            "1256/1256 [==============================] - 9s 7ms/step - loss: 1.0033 - accuracy: 0.4840 - val_loss: 0.8494 - val_accuracy: 0.7193\n",
            "Epoch 6/50\n",
            "1256/1256 [==============================] - 10s 8ms/step - loss: 0.9848 - accuracy: 0.5036 - val_loss: 0.8087 - val_accuracy: 0.7520\n",
            "Epoch 7/50\n",
            "1256/1256 [==============================] - 9s 7ms/step - loss: 0.9572 - accuracy: 0.5238 - val_loss: 1.0116 - val_accuracy: 0.5184\n",
            "Epoch 8/50\n",
            "1256/1256 [==============================] - 9s 7ms/step - loss: 0.9371 - accuracy: 0.5365 - val_loss: 0.7754 - val_accuracy: 0.7352\n",
            "Epoch 9/50\n",
            "1256/1256 [==============================] - 9s 7ms/step - loss: 1.0305 - accuracy: 0.5025 - val_loss: 0.9356 - val_accuracy: 0.7095\n",
            "Epoch 10/50\n",
            "1256/1256 [==============================] - 10s 8ms/step - loss: 1.0191 - accuracy: 0.4721 - val_loss: 0.9919 - val_accuracy: 0.7143\n",
            "Epoch 11/50\n",
            "1256/1256 [==============================] - 9s 7ms/step - loss: 1.0069 - accuracy: 0.4767 - val_loss: 1.0068 - val_accuracy: 0.5911\n",
            "Epoch 12/50\n",
            "1256/1256 [==============================] - 9s 7ms/step - loss: 1.0029 - accuracy: 0.4791 - val_loss: 0.9290 - val_accuracy: 0.6666\n",
            "Epoch 13/50\n",
            "1256/1256 [==============================] - 9s 7ms/step - loss: 1.0011 - accuracy: 0.4822 - val_loss: 0.8978 - val_accuracy: 0.6258\n",
            "Epoch 14/50\n",
            "1256/1256 [==============================] - 9s 7ms/step - loss: 1.0011 - accuracy: 0.4818 - val_loss: 0.8854 - val_accuracy: 0.6175\n",
            "Epoch 15/50\n",
            "1256/1256 [==============================] - 8s 7ms/step - loss: 0.9981 - accuracy: 0.4861 - val_loss: 0.8497 - val_accuracy: 0.6924\n",
            "Epoch 16/50\n",
            "1256/1256 [==============================] - 10s 8ms/step - loss: 0.9794 - accuracy: 0.5011 - val_loss: 0.8742 - val_accuracy: 0.6639\n",
            "Epoch 17/50\n",
            "1256/1256 [==============================] - 9s 7ms/step - loss: 0.9138 - accuracy: 0.5536 - val_loss: 0.7914 - val_accuracy: 0.7773\n",
            "Epoch 18/50\n",
            "1256/1256 [==============================] - 8s 7ms/step - loss: 0.8749 - accuracy: 0.5775 - val_loss: 0.7376 - val_accuracy: 0.7973\n",
            "Epoch 19/50\n",
            "1256/1256 [==============================] - 10s 8ms/step - loss: 0.8521 - accuracy: 0.5870 - val_loss: 0.7302 - val_accuracy: 0.7960\n",
            "Epoch 20/50\n",
            "1256/1256 [==============================] - 9s 8ms/step - loss: 0.8516 - accuracy: 0.5893 - val_loss: 0.7594 - val_accuracy: 0.7714\n",
            "Epoch 21/50\n",
            "1256/1256 [==============================] - 9s 7ms/step - loss: 0.8432 - accuracy: 0.5964 - val_loss: 0.7346 - val_accuracy: 0.7856\n",
            "Epoch 22/50\n",
            "1256/1256 [==============================] - 9s 7ms/step - loss: 0.8386 - accuracy: 0.5971 - val_loss: 0.7279 - val_accuracy: 0.7796\n",
            "Epoch 23/50\n",
            "1256/1256 [==============================] - 9s 7ms/step - loss: 0.8322 - accuracy: 0.6044 - val_loss: 0.7678 - val_accuracy: 0.7403\n",
            "Epoch 24/50\n",
            "1256/1256 [==============================] - 9s 7ms/step - loss: 0.8315 - accuracy: 0.6072 - val_loss: 0.7210 - val_accuracy: 0.7883\n",
            "Epoch 25/50\n",
            "1256/1256 [==============================] - 8s 7ms/step - loss: 0.8266 - accuracy: 0.6103 - val_loss: 0.7919 - val_accuracy: 0.7535\n",
            "Epoch 26/50\n",
            "1256/1256 [==============================] - 9s 7ms/step - loss: 0.8238 - accuracy: 0.6106 - val_loss: 0.7529 - val_accuracy: 0.7594\n",
            "Epoch 27/50\n",
            "1256/1256 [==============================] - 9s 7ms/step - loss: 0.8171 - accuracy: 0.6131 - val_loss: 0.7485 - val_accuracy: 0.7309\n",
            "Epoch 28/50\n",
            "1256/1256 [==============================] - 8s 7ms/step - loss: 0.8159 - accuracy: 0.6198 - val_loss: 0.6953 - val_accuracy: 0.7754\n",
            "Epoch 29/50\n",
            "1256/1256 [==============================] - 9s 7ms/step - loss: 0.8104 - accuracy: 0.6228 - val_loss: 0.6989 - val_accuracy: 0.7821\n",
            "Epoch 30/50\n",
            "1256/1256 [==============================] - 9s 7ms/step - loss: 0.8036 - accuracy: 0.6269 - val_loss: 0.6673 - val_accuracy: 0.7851\n",
            "Epoch 31/50\n",
            "1256/1256 [==============================] - 10s 8ms/step - loss: 0.7997 - accuracy: 0.6278 - val_loss: 0.6474 - val_accuracy: 0.8009\n",
            "Epoch 32/50\n",
            "1256/1256 [==============================] - 8s 7ms/step - loss: 0.7954 - accuracy: 0.6297 - val_loss: 0.6787 - val_accuracy: 0.7728\n",
            "Epoch 33/50\n",
            "1256/1256 [==============================] - 9s 7ms/step - loss: 0.7904 - accuracy: 0.6339 - val_loss: 0.7894 - val_accuracy: 0.7309\n",
            "Epoch 34/50\n",
            "1256/1256 [==============================] - 9s 7ms/step - loss: 0.7891 - accuracy: 0.6340 - val_loss: 0.7218 - val_accuracy: 0.7611\n",
            "Epoch 35/50\n",
            "1256/1256 [==============================] - 8s 7ms/step - loss: 0.7825 - accuracy: 0.6418 - val_loss: 0.8290 - val_accuracy: 0.6857\n",
            "Epoch 36/50\n",
            "1256/1256 [==============================] - 10s 8ms/step - loss: 0.7814 - accuracy: 0.6386 - val_loss: 0.8685 - val_accuracy: 0.6440\n",
            "Epoch 37/50\n",
            "1256/1256 [==============================] - 9s 7ms/step - loss: 0.7703 - accuracy: 0.6483 - val_loss: 0.6406 - val_accuracy: 0.7860\n",
            "Epoch 38/50\n",
            "1256/1256 [==============================] - 8s 7ms/step - loss: 0.7664 - accuracy: 0.6512 - val_loss: 0.6632 - val_accuracy: 0.7754\n",
            "Epoch 39/50\n",
            "1256/1256 [==============================] - 9s 7ms/step - loss: 0.7561 - accuracy: 0.6585 - val_loss: 0.6928 - val_accuracy: 0.7401\n",
            "Epoch 40/50\n",
            "1256/1256 [==============================] - 9s 7ms/step - loss: 0.7448 - accuracy: 0.6663 - val_loss: 0.6482 - val_accuracy: 0.7739\n",
            "Epoch 41/50\n",
            "1256/1256 [==============================] - 8s 7ms/step - loss: 0.7434 - accuracy: 0.6670 - val_loss: 0.6333 - val_accuracy: 0.7860\n",
            "Epoch 42/50\n",
            "1256/1256 [==============================] - 9s 7ms/step - loss: 0.7317 - accuracy: 0.6758 - val_loss: 0.7192 - val_accuracy: 0.7410\n",
            "Epoch 43/50\n",
            "1256/1256 [==============================] - 9s 7ms/step - loss: 0.7240 - accuracy: 0.6831 - val_loss: 0.6671 - val_accuracy: 0.7723\n",
            "Epoch 44/50\n",
            "1256/1256 [==============================] - 9s 7ms/step - loss: 0.7219 - accuracy: 0.6820 - val_loss: 0.6602 - val_accuracy: 0.7627\n",
            "Epoch 45/50\n",
            "1256/1256 [==============================] - 9s 7ms/step - loss: 0.7104 - accuracy: 0.6888 - val_loss: 0.7873 - val_accuracy: 0.6857\n",
            "Epoch 46/50\n",
            "1256/1256 [==============================] - 9s 7ms/step - loss: 0.7115 - accuracy: 0.6884 - val_loss: 0.9177 - val_accuracy: 0.4847\n",
            "Epoch 47/50\n",
            "1256/1256 [==============================] - 9s 7ms/step - loss: 0.7087 - accuracy: 0.6905 - val_loss: 0.6764 - val_accuracy: 0.7610\n",
            "Epoch 48/50\n",
            "1256/1256 [==============================] - 8s 7ms/step - loss: 0.7032 - accuracy: 0.6929 - val_loss: 0.6218 - val_accuracy: 0.7562\n",
            "Epoch 49/50\n",
            "1256/1256 [==============================] - 9s 7ms/step - loss: 0.7052 - accuracy: 0.6926 - val_loss: 0.6119 - val_accuracy: 0.7863\n",
            "Epoch 50/50\n",
            "1256/1256 [==============================] - 9s 7ms/step - loss: 0.6973 - accuracy: 0.6972 - val_loss: 0.6264 - val_accuracy: 0.7855\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7d88d538c610>"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_gru = gru.predict(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L7LoSqcHOCJ8",
        "outputId": "9057ef2c-29f2-41f7-f701-72b962a37897"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "233/233 [==============================] - 2s 5ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_gru_classes = np.argmax(y_pred_gru, axis = 1)\n",
        "set(y_pred_gru_classes)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q2JzR948P3N9",
        "outputId": "03a6f588-7d99-4e5d-a264-b3c37428f72e"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{0, 1, 2}"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test_classes, y_pred_gru_classes))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cidvf6PvQAca",
        "outputId": "b585b6ef-907d-4442-8742-9a208f59bab5"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.20      0.38      0.26       424\n",
            "           1       0.96      0.81      0.88      5796\n",
            "           2       0.57      0.79      0.66      1215\n",
            "\n",
            "    accuracy                           0.79      7435\n",
            "   macro avg       0.57      0.66      0.60      7435\n",
            "weighted avg       0.85      0.79      0.81      7435\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Metrics for GRU\n",
        "'''\n",
        "              precision    recall  f1-score   support\n",
        "\n",
        "           0       0.38      0.20      0.26       823\n",
        "           1       0.81      0.96      0.88      4941\n",
        "           2       0.79      0.57      0.66      1671\n",
        "\n",
        "    accuracy                           0.79      7435\n",
        "   macro avg       0.66      0.57      0.60      7435\n",
        "weighted avg       0.76      0.79      0.76      7435\n",
        "'''"
      ],
      "metadata": {
        "id": "SbyvJ3cxQND8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Bi-Directional**"
      ],
      "metadata": {
        "id": "ZPXy3GdCRe9w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "bi_lstm = Sequential()\n",
        "bi_lstm.add(Bidirectional(LSTM(128, return_sequences=False), input_shape=(100, 1)))\n",
        "bi_lstm.add(Dense(64, activation='relu'))\n",
        "bi_lstm.add(Dropout(0.2))\n",
        "bi_lstm.add(Dense(3, activation='softmax'))\n",
        "\n",
        "bi_lstm.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ypLdmvIHRc2v",
        "outputId": "903bb21f-5f78-436d-ede6-c81c9c2e15e0"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_6\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " bidirectional_2 (Bidirecti  (None, 256)               133120    \n",
            " onal)                                                           \n",
            "                                                                 \n",
            " dense_10 (Dense)            (None, 64)                16448     \n",
            "                                                                 \n",
            " dropout_5 (Dropout)         (None, 64)                0         \n",
            "                                                                 \n",
            " dense_11 (Dense)            (None, 3)                 195       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 149763 (585.01 KB)\n",
            "Trainable params: 149763 (585.01 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bi_lstm.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "KXESYm1JSQn4"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bi_lstm.fit(X_train_res,y_train_res,epochs=50,validation_data=(X_test,y_test), batch_size = 32)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tMA1JnnvSn4b",
        "outputId": "58a908fb-8c6b-4972-d7a0-c7a7308489ec"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "1256/1256 [==============================] - 22s 12ms/step - loss: 1.0128 - accuracy: 0.4769 - val_loss: 0.8748 - val_accuracy: 0.7088\n",
            "Epoch 2/50\n",
            "1256/1256 [==============================] - 15s 12ms/step - loss: 0.9962 - accuracy: 0.4935 - val_loss: 0.8144 - val_accuracy: 0.7411\n",
            "Epoch 3/50\n",
            "1256/1256 [==============================] - 14s 11ms/step - loss: 0.9292 - accuracy: 0.5448 - val_loss: 0.7349 - val_accuracy: 0.7965\n",
            "Epoch 4/50\n",
            "1256/1256 [==============================] - 14s 12ms/step - loss: 0.8998 - accuracy: 0.5591 - val_loss: 0.8334 - val_accuracy: 0.7267\n",
            "Epoch 5/50\n",
            "1256/1256 [==============================] - 15s 12ms/step - loss: 0.8835 - accuracy: 0.5662 - val_loss: 0.7863 - val_accuracy: 0.7465\n",
            "Epoch 6/50\n",
            "1256/1256 [==============================] - 15s 12ms/step - loss: 0.8746 - accuracy: 0.5730 - val_loss: 0.6904 - val_accuracy: 0.8098\n",
            "Epoch 7/50\n",
            "1256/1256 [==============================] - 15s 12ms/step - loss: 0.8677 - accuracy: 0.5752 - val_loss: 0.7904 - val_accuracy: 0.7606\n",
            "Epoch 8/50\n",
            "1256/1256 [==============================] - 15s 12ms/step - loss: 0.8571 - accuracy: 0.5814 - val_loss: 0.7563 - val_accuracy: 0.7892\n",
            "Epoch 9/50\n",
            "1256/1256 [==============================] - 15s 12ms/step - loss: 0.8525 - accuracy: 0.5813 - val_loss: 0.7542 - val_accuracy: 0.7919\n",
            "Epoch 10/50\n",
            "1256/1256 [==============================] - 15s 12ms/step - loss: 0.8463 - accuracy: 0.5865 - val_loss: 0.7726 - val_accuracy: 0.8078\n",
            "Epoch 11/50\n",
            "1256/1256 [==============================] - 16s 13ms/step - loss: 0.8427 - accuracy: 0.5886 - val_loss: 0.6844 - val_accuracy: 0.8094\n",
            "Epoch 12/50\n",
            "1256/1256 [==============================] - 15s 12ms/step - loss: 0.8385 - accuracy: 0.5934 - val_loss: 0.9546 - val_accuracy: 0.6492\n",
            "Epoch 13/50\n",
            "1256/1256 [==============================] - 14s 12ms/step - loss: 0.8360 - accuracy: 0.5908 - val_loss: 0.7112 - val_accuracy: 0.7820\n",
            "Epoch 14/50\n",
            "1256/1256 [==============================] - 15s 12ms/step - loss: 0.8274 - accuracy: 0.5987 - val_loss: 0.6865 - val_accuracy: 0.8102\n",
            "Epoch 15/50\n",
            "1256/1256 [==============================] - 15s 12ms/step - loss: 0.8254 - accuracy: 0.5995 - val_loss: 0.6656 - val_accuracy: 0.7914\n",
            "Epoch 16/50\n",
            "1256/1256 [==============================] - 15s 12ms/step - loss: 0.8215 - accuracy: 0.6032 - val_loss: 0.7554 - val_accuracy: 0.7796\n",
            "Epoch 17/50\n",
            "1256/1256 [==============================] - 14s 12ms/step - loss: 0.8168 - accuracy: 0.6065 - val_loss: 0.8335 - val_accuracy: 0.7166\n",
            "Epoch 18/50\n",
            "1256/1256 [==============================] - 15s 12ms/step - loss: 0.8138 - accuracy: 0.6081 - val_loss: 0.7485 - val_accuracy: 0.7794\n",
            "Epoch 19/50\n",
            "1256/1256 [==============================] - 15s 12ms/step - loss: 0.8082 - accuracy: 0.6126 - val_loss: 0.7109 - val_accuracy: 0.7809\n",
            "Epoch 20/50\n",
            "1256/1256 [==============================] - 15s 12ms/step - loss: 0.8045 - accuracy: 0.6162 - val_loss: 0.6196 - val_accuracy: 0.7935\n",
            "Epoch 21/50\n",
            "1256/1256 [==============================] - 15s 12ms/step - loss: 0.8035 - accuracy: 0.6157 - val_loss: 0.6867 - val_accuracy: 0.8028\n",
            "Epoch 22/50\n",
            "1256/1256 [==============================] - 15s 12ms/step - loss: 0.8041 - accuracy: 0.6169 - val_loss: 0.7456 - val_accuracy: 0.7847\n",
            "Epoch 23/50\n",
            "1256/1256 [==============================] - 15s 12ms/step - loss: 0.8003 - accuracy: 0.6208 - val_loss: 0.6825 - val_accuracy: 0.8106\n",
            "Epoch 24/50\n",
            "1256/1256 [==============================] - 15s 12ms/step - loss: 0.7953 - accuracy: 0.6221 - val_loss: 0.6927 - val_accuracy: 0.7888\n",
            "Epoch 25/50\n",
            "1256/1256 [==============================] - 14s 11ms/step - loss: 0.7955 - accuracy: 0.6236 - val_loss: 0.6800 - val_accuracy: 0.7855\n",
            "Epoch 26/50\n",
            "1256/1256 [==============================] - 15s 12ms/step - loss: 0.7942 - accuracy: 0.6233 - val_loss: 0.6395 - val_accuracy: 0.8024\n",
            "Epoch 27/50\n",
            "1256/1256 [==============================] - 16s 13ms/step - loss: 0.7906 - accuracy: 0.6253 - val_loss: 0.6837 - val_accuracy: 0.7833\n",
            "Epoch 28/50\n",
            "1256/1256 [==============================] - 14s 12ms/step - loss: 0.7832 - accuracy: 0.6300 - val_loss: 0.6184 - val_accuracy: 0.7934\n",
            "Epoch 29/50\n",
            "1256/1256 [==============================] - 16s 12ms/step - loss: 0.7835 - accuracy: 0.6290 - val_loss: 0.6874 - val_accuracy: 0.7948\n",
            "Epoch 30/50\n",
            "1256/1256 [==============================] - 14s 12ms/step - loss: 0.7852 - accuracy: 0.6286 - val_loss: 0.7128 - val_accuracy: 0.7701\n",
            "Epoch 31/50\n",
            "1256/1256 [==============================] - 14s 11ms/step - loss: 0.7788 - accuracy: 0.6332 - val_loss: 0.7098 - val_accuracy: 0.7837\n",
            "Epoch 32/50\n",
            "1256/1256 [==============================] - 14s 11ms/step - loss: 0.7755 - accuracy: 0.6354 - val_loss: 0.6205 - val_accuracy: 0.8000\n",
            "Epoch 33/50\n",
            "1256/1256 [==============================] - 16s 13ms/step - loss: 0.7955 - accuracy: 0.6322 - val_loss: 0.6892 - val_accuracy: 0.7719\n",
            "Epoch 34/50\n",
            "1256/1256 [==============================] - 15s 12ms/step - loss: 0.7782 - accuracy: 0.6337 - val_loss: 0.6299 - val_accuracy: 0.7980\n",
            "Epoch 35/50\n",
            "1256/1256 [==============================] - 15s 12ms/step - loss: 0.7750 - accuracy: 0.6350 - val_loss: 0.6590 - val_accuracy: 0.7672\n",
            "Epoch 36/50\n",
            "1256/1256 [==============================] - 14s 11ms/step - loss: 0.7744 - accuracy: 0.6347 - val_loss: 0.7282 - val_accuracy: 0.7505\n",
            "Epoch 37/50\n",
            "1256/1256 [==============================] - 15s 12ms/step - loss: 0.7736 - accuracy: 0.6367 - val_loss: 0.7084 - val_accuracy: 0.7661\n",
            "Epoch 38/50\n",
            "1256/1256 [==============================] - 15s 12ms/step - loss: 0.7679 - accuracy: 0.6429 - val_loss: 0.7451 - val_accuracy: 0.7431\n",
            "Epoch 39/50\n",
            "1256/1256 [==============================] - 14s 11ms/step - loss: 0.7667 - accuracy: 0.6435 - val_loss: 0.7005 - val_accuracy: 0.7637\n",
            "Epoch 40/50\n",
            "1256/1256 [==============================] - 16s 13ms/step - loss: 0.7664 - accuracy: 0.6438 - val_loss: 0.5759 - val_accuracy: 0.7981\n",
            "Epoch 41/50\n",
            "1256/1256 [==============================] - 15s 12ms/step - loss: 0.7629 - accuracy: 0.6429 - val_loss: 0.6541 - val_accuracy: 0.7835\n",
            "Epoch 42/50\n",
            "1256/1256 [==============================] - 20s 16ms/step - loss: 0.7614 - accuracy: 0.6474 - val_loss: 0.7195 - val_accuracy: 0.7459\n",
            "Epoch 43/50\n",
            "1256/1256 [==============================] - 24s 19ms/step - loss: 0.7599 - accuracy: 0.6472 - val_loss: 0.6330 - val_accuracy: 0.7891\n",
            "Epoch 44/50\n",
            "1256/1256 [==============================] - 17s 14ms/step - loss: 0.7596 - accuracy: 0.6463 - val_loss: 0.5932 - val_accuracy: 0.7841\n",
            "Epoch 45/50\n",
            "1256/1256 [==============================] - 16s 13ms/step - loss: 0.7591 - accuracy: 0.6462 - val_loss: 0.6141 - val_accuracy: 0.7900\n",
            "Epoch 46/50\n",
            "1256/1256 [==============================] - 23s 18ms/step - loss: 0.7541 - accuracy: 0.6506 - val_loss: 0.6272 - val_accuracy: 0.7759\n",
            "Epoch 47/50\n",
            "1256/1256 [==============================] - 21s 17ms/step - loss: 0.7531 - accuracy: 0.6501 - val_loss: 0.7445 - val_accuracy: 0.7406\n",
            "Epoch 48/50\n",
            "1256/1256 [==============================] - 15s 12ms/step - loss: 0.7523 - accuracy: 0.6517 - val_loss: 0.6030 - val_accuracy: 0.8000\n",
            "Epoch 49/50\n",
            "1256/1256 [==============================] - 14s 11ms/step - loss: 0.7510 - accuracy: 0.6528 - val_loss: 0.7428 - val_accuracy: 0.7216\n",
            "Epoch 50/50\n",
            "1256/1256 [==============================] - 14s 12ms/step - loss: 0.7505 - accuracy: 0.6520 - val_loss: 0.7267 - val_accuracy: 0.7488\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7d885a499de0>"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_bi = bi_lstm.predict(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZU1J0MSlSxRl",
        "outputId": "e3ae641b-844e-4469-c011-fa276075772f"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "233/233 [==============================] - 2s 5ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_bi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zqEcD94Marzw",
        "outputId": "3edf53fa-2d6a-4d45-bd99-9a796cadc6ce"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.19176336, 0.8004061 , 0.00783056],\n",
              "       [0.22486626, 0.05454149, 0.72059226],\n",
              "       [0.48120147, 0.19591534, 0.32288313],\n",
              "       ...,\n",
              "       [0.07380065, 0.01975142, 0.9064479 ],\n",
              "       [0.39131323, 0.07861749, 0.5300693 ],\n",
              "       [0.20821561, 0.04307014, 0.74871427]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_bi_classes = np.argmax(y_pred_bi, axis =1)\n",
        "set(y_pred_bi_classes)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N5DdYExLau3a",
        "outputId": "0efd3fef-9106-4097-c2c1-bc629093d35f"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{0, 1, 2}"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test_classes, y_pred_bi_classes))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CfqpkIvQbaTG",
        "outputId": "99957ca4-8960-4d85-8baf-794fa3175eec"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.13      0.32      0.18       424\n",
            "           1       0.96      0.77      0.85      5796\n",
            "           2       0.56      0.81      0.66      1215\n",
            "\n",
            "    accuracy                           0.75      7435\n",
            "   macro avg       0.55      0.63      0.57      7435\n",
            "weighted avg       0.85      0.75      0.78      7435\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Metrics for Bi-LSTM\n",
        "'''\n",
        "              precision    recall  f1-score   support\n",
        "\n",
        "           0       0.13      0.32      0.18       424\n",
        "           1       0.96      0.77      0.85      5796\n",
        "           2       0.56      0.81      0.66      1215\n",
        "\n",
        "    accuracy                           0.75      7435\n",
        "   macro avg       0.55      0.63      0.57      7435\n",
        "weighted avg       0.85      0.75      0.78      7435\n",
        "'''\n"
      ],
      "metadata": {
        "id": "MI0DP2C7b2yO"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}