RNN_Embedding_tokenizer.ipynb

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    "accelerator": "GPU",
    "gpuClass": "standard"
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  "cells": [
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-vh49JgejBe9",
        "outputId": "8f0739d6-cfcb-4de6-f7ff-d8cc56cdc48b"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install Sastrawi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BpJ5fjsRL7bb",
        "outputId": "a6b67064-6632-4d9e-b7ea-998600b176e4"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting Sastrawi\n",
            "  Downloading Sastrawi-1.0.1-py2.py3-none-any.whl (209 kB)\n",
            "\u001b[K     |████████████████████████████████| 209 kB 19.7 MB/s \n",
            "\u001b[?25hInstalling collected packages: Sastrawi\n",
            "Successfully installed Sastrawi-1.0.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "QW_iby7GK_zK"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib as mpl\n",
        "import seaborn as sns\n",
        "from PIL import Image \n",
        "# un comment if you already download it\n",
        "# import nltk\n",
        "# nltk.download('stopwords')\n",
        "# nltk.download('punkt')\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from wordcloud import WordCloud\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.tokenize import word_tokenize\n",
        "from Sastrawi.Stemmer.StemmerFactory import StemmerFactory\n",
        "import string\n",
        "import re\n",
        "import html\n",
        "\n",
        "\n",
        "#saving model\n",
        "import pickle\n",
        "\n",
        "#inference\n",
        "import json\n",
        "import requests\n",
        "# Model Evaluation\n",
        "from sklearn.metrics import ConfusionMatrixDisplay,confusion_matrix,classification_report\n",
        "\n",
        "# Modelling \n",
        "from tensorflow.keras.layers import Dense, GlobalAveragePooling1D,Bidirectional, Input, LSTM, GRU\n",
        "from tensorflow.keras.models import Model, Sequential\n",
        "from tensorflow.keras.layers import TextVectorization, Embedding\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "from tensorflow import keras\n",
        "import tensorflow as tf"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train = pd.read_csv('/content/drive/MyDrive/7015-group-project-main/7015-group-project-main/X_train.csv')\n",
        "x_valid = pd.read_csv('/content/drive/MyDrive/7015-group-project-main/7015-group-project-main/X_val.csv')\n",
        "y_train = pd.read_csv('/content/drive/MyDrive/7015-group-project-main/7015-group-project-main/y_train.csv')\n",
        "y_valid = pd.read_csv('/content/drive/MyDrive/7015-group-project-main/7015-group-project-main/y_val.csv')"
      ],
      "metadata": {
        "id": "yesxH1TlLub0"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train"
      ],
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          "height": 423
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        "id": "eU2wRQeqVIIT",
        "outputId": "b21130dd-0835-4d38-905a-4957dd222bd2"
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            "text/plain": [
              "        id              keyword            location  \\\n",
              "0     2714              crushed         Chicago, IL   \n",
              "1     7361           obliterate         Cymru araul   \n",
              "2     1290               bloody             Chicago   \n",
              "3     1932  burning%20buildings                 NaN   \n",
              "4     4041             disaster         Calgary, AB   \n",
              "...    ...                  ...                 ...   \n",
              "6085  7532          oil%20spill    Street of Dallas   \n",
              "6086  1821               burned  toronto Û¢ dallas   \n",
              "6087  9504            terrorist                 NaN   \n",
              "6088  2797               curfew            IM LOST    \n",
              "6089  3667              destroy                 NaN   \n",
              "\n",
              "                                           cleaned_text  \n",
              "0       jimmyfallon crush squirrel bone mortar pestl...  \n",
              "1      mccainenl think spectacular look stonewal rio...  \n",
              "2     can t bloodi wait   soni set date stephen king...  \n",
              "3     protest ralli stone mountain  atleast they r b...  \n",
              "4      rbcinsur quot websit   disaster  tri 3 browse...  \n",
              "...                                                 ...  \n",
              "6085     refugio oil spill may costlier bigger project   \n",
              "6086  hate im tri straighten hair brother come swoop...  \n",
              "6087               pull fight hoe call zayn terrorist    \n",
              "6088             da judg gave di girl 5pm curfew         \n",
              "6089  get   150 free bet regist boylesport casino lu...  \n",
              "\n",
              "[6090 rows x 4 columns]"
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              "      <td>2714</td>\n",
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              "      <td>jimmyfallon crush squirrel bone mortar pestl...</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7361</td>\n",
              "      <td>obliterate</td>\n",
              "      <td>Cymru araul</td>\n",
              "      <td>mccainenl think spectacular look stonewal rio...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1290</td>\n",
              "      <td>bloody</td>\n",
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              "      <td>can t bloodi wait   soni set date stephen king...</td>\n",
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              "      <td>protest ralli stone mountain  atleast they r b...</td>\n",
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              "      <td>rbcinsur quot websit   disaster  tri 3 browse...</td>\n",
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              "      <th>6085</th>\n",
              "      <td>7532</td>\n",
              "      <td>oil%20spill</td>\n",
              "      <td>Street of Dallas</td>\n",
              "      <td>refugio oil spill may costlier bigger project</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6086</th>\n",
              "      <td>1821</td>\n",
              "      <td>burned</td>\n",
              "      <td>toronto Û¢ dallas</td>\n",
              "      <td>hate im tri straighten hair brother come swoop...</td>\n",
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              "    <tr>\n",
              "      <th>6087</th>\n",
              "      <td>9504</td>\n",
              "      <td>terrorist</td>\n",
              "      <td>NaN</td>\n",
              "      <td>pull fight hoe call zayn terrorist</td>\n",
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              "    <tr>\n",
              "      <th>6088</th>\n",
              "      <td>2797</td>\n",
              "      <td>curfew</td>\n",
              "      <td>IM LOST</td>\n",
              "      <td>da judg gave di girl 5pm curfew</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6089</th>\n",
              "      <td>3667</td>\n",
              "      <td>destroy</td>\n",
              "      <td>NaN</td>\n",
              "      <td>get   150 free bet regist boylesport casino lu...</td>\n",
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              "<p>6090 rows × 4 columns</p>\n",
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          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "x_train_final = x_train['cleaned_text'].to_list()\n",
        "x_valid_final = x_valid['cleaned_text'].to_list()\n",
        "# x_train_final = np.asarray(x_train).astype('float32')\n",
        "# x_valid_final = np.asarray(x_valid).astype('float32')\n",
        "x_train_final[-5:]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n4_EoWSKVRE-",
        "outputId": "3cdb5dce-a2d3-4826-8773-fd6219133c96"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['refugio oil spill may costlier bigger project ',\n",
              " 'hate im tri straighten hair brother come swoop behind scare burn finger',\n",
              " 'pull fight hoe call zayn terrorist  ',\n",
              " 'da judg gave di girl 5pm curfew       ',\n",
              " 'get   150 free bet regist boylesport casino luck destroy  ']"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# y_test_final = to_categorical(y_test)\n",
        "y_train_final = to_categorical(y_train['target'])\n",
        "y_valid_final = to_categorical(y_valid['target'])\n",
        "# y_train_final = y_train['target'].to_list()\n",
        "# y_valid_final = y_valid['target'].to_list()\n",
        "# y_valid_final[-5:]\n",
        "y_train_final"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nVFNBeRwSkXM",
        "outputId": "bad8e5a8-7983-4427-c652-89e18c890682"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1., 0.],\n",
              "       [0., 1.],\n",
              "       [1., 0.],\n",
              "       ...,\n",
              "       [0., 1.],\n",
              "       [1., 0.],\n",
              "       [1., 0.]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "keras.backend.clear_session()\n",
        "np.random.seed(12)\n",
        "tf.random.set_seed(12)"
      ],
      "metadata": {
        "id": "J9YtxO0RSrY2"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "AUTOTUNE = tf.data.AUTOTUNE # auto tune prefetch\n",
        "batch_size = 128\n",
        "\n",
        "# data_batches = tf.data.Dataset.from_tensor_slices((x_train_trans, y_train_final)).shuffled_data = preprocessed_data.shuffle(shuffle_buffer_size)\n",
        "\n",
        "\n",
        "train_final = tf.data.Dataset.from_tensor_slices((x_train_final, y_train_final)).batch(batch_size).shuffle(64).cache().prefetch(AUTOTUNE)\n",
        "# test_final = tf.data.Dataset.from_tensor_slices((X_test, y_test_final)).batch(batch_size).cache().prefetch(AUTOTUNE)\n",
        "val_final = tf.data.Dataset.from_tensor_slices((x_valid_final, y_valid_final)).batch(batch_size).shuffle(64).cache().prefetch(AUTOTUNE)"
      ],
      "metadata": {
        "id": "71osEEuDSurM"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Define Vectorize \n",
        "Vectorize = CountVectorizer()\n",
        "# Fit only on X_train, and transform X_train\n",
        "X_train_vec = Vectorize.fit_transform(x_train_final)\n",
        "# transform data validation\n",
        "X_train_vec = Vectorize.transform(x_train_final)\n",
        "X_valid_vec = Vectorize.transform(x_valid_final)\n",
        "\n",
        "# count Vocab from the vectorizer\n",
        "count_vocab = len(Vectorize.vocabulary_.keys())\n",
        "# maximum word count of the documents\n",
        "max_word_count = max([len(i.split(\" \")) for i in x_train_final])\n",
        "\n",
        "print(\"Count Vocabs : \",count_vocab)\n",
        "print(\"Max Count In a Document :\",max_word_count,\"word\")\n",
        "\n",
        "vectorize_layer = TextVectorization(\n",
        "    max_tokens=count_vocab,\n",
        "    split=\"whitespace\",\n",
        "    ngrams=None,\n",
        "    output_mode='int',\n",
        "    output_sequence_length=max_word_count,\n",
        "    input_shape=(1,))\n",
        "    \n",
        "\n",
        "vectorize_layer.adapt(x_train_final)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eSecUP55YLIA",
        "outputId": "db4ba45e-877f-42b8-f1d9-0eaa3339aeab"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Count Vocabs :  13341\n",
            "Max Count In a Document : 75 word\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Embedding\n",
        "\n",
        "embedding = Embedding(input_dim=count_vocab,\n",
        "                      output_dim=128,\n",
        "                      embeddings_initializer=\"uniform\",\n",
        "                      input_length=max_word_count)"
      ],
      "metadata": {
        "id": "D90AV29jYR4p"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# callback for stopping early epochs with restore_best_weights\n",
        "early_stopping_cb = keras.callbacks.EarlyStopping(patience=10,\n",
        "                                                  restore_best_weights=True)"
      ],
      "metadata": {
        "id": "2ke-TpquYR2P"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from keras import backend as K\n",
        "def recall_m(y_true, y_pred):\n",
        "    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n",
        "    possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))\n",
        "    recall = true_positives / (possible_positives + K.epsilon())\n",
        "    return recall\n",
        "\n",
        "def precision_m(y_true, y_pred):\n",
        "    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n",
        "    predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))\n",
        "    precision = true_positives / (predicted_positives + K.epsilon())\n",
        "    return precision\n",
        "\n",
        "def f1_m(y_true, y_pred):\n",
        "    precision = precision_m(y_true, y_pred)\n",
        "    recall = recall_m(y_true, y_pred)\n",
        "    return 2*((precision*recall)/(precision+recall+K.epsilon()))"
      ],
      "metadata": {
        "id": "ORoVv56FTiWr"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_rnn = Sequential()\n",
        "model_rnn.add(vectorize_layer)\n",
        "model_rnn.add(embedding)\n",
        "model_rnn.add(GlobalAveragePooling1D())\n",
        "model_rnn.add(Dense(144,activation='relu'))\n",
        "model_rnn.add(Dense(72,activation='relu'))\n",
        "model_rnn.add(Dense(27,activation='relu'))\n",
        "model_rnn.add(Dense(9,activation='relu'))\n",
        "model_rnn.add(Dense(2,activation='softmax'))\n",
        "\n",
        "model_rnn.compile(loss='CategoricalCrossentropy',optimizer='adamax',metrics=[f1_m,precision_m, recall_m])"
      ],
      "metadata": {
        "id": "mIL05AdiYR0A"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_rnn.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RkRtwbLqYRx9",
        "outputId": "2858afa8-dd91-49aa-da9c-08c3eabc6b6f"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " text_vectorization (TextVec  (None, 75)               0         \n",
            " torization)                                                     \n",
            "                                                                 \n",
            " embedding (Embedding)       (None, 75, 128)           1707648   \n",
            "                                                                 \n",
            " global_average_pooling1d (G  (None, 128)              0         \n",
            " lobalAveragePooling1D)                                          \n",
            "                                                                 \n",
            " dense (Dense)               (None, 144)               18576     \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 72)                10440     \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 27)                1971      \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 9)                 252       \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 2)                 20        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 1,738,907\n",
            "Trainable params: 1,738,907\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_rnn_opt = Sequential()\n",
        "model_rnn_opt.add(vectorize_layer)\n",
        "model_rnn_opt.add(embedding)\n",
        "model_rnn_opt.add(GlobalAveragePooling1D())\n",
        "\n",
        "model_rnn_opt.add(Dense(128,kernel_regularizer=keras.regularizers.l2(),kernel_initializer=keras.initializers.LecunUniform(seed=12)))\n",
        "model_rnn_opt.add(keras.layers.Dropout(rate=0.52))\n",
        "model_rnn_opt.add(tf.keras.layers.BatchNormalization())\n",
        "model_rnn_opt.add(keras.layers.Activation('selu'))\n",
        "\n",
        "model_rnn_opt.add(Dense(64,kernel_regularizer=keras.regularizers.l2(),kernel_initializer=keras.initializers.LecunUniform(seed=12)))\n",
        "model_rnn_opt.add(keras.layers.Dropout(rate=.52))\n",
        "model_rnn_opt.add(tf.keras.layers.BatchNormalization())\n",
        "model_rnn_opt.add(keras.layers.Activation('selu'))\n",
        "\n",
        "model_rnn_opt.add(Dense(32,kernel_regularizer=keras.regularizers.l2(),kernel_initializer=keras.initializers.LecunUniform(seed=12)))\n",
        "model_rnn_opt.add(keras.layers.Dropout(rate=0.52))\n",
        "model_rnn_opt.add(tf.keras.layers.BatchNormalization())\n",
        "model_rnn_opt.add(keras.layers.Activation('selu'))\n",
        "\n",
        "model_rnn_opt.add(Dense(16,kernel_regularizer=keras.regularizers.l2(),kernel_initializer=keras.initializers.LecunUniform(seed=12)))\n",
        "model_rnn_opt.add(keras.layers.Dropout(rate=0.52))\n",
        "model_rnn_opt.add(tf.keras.layers.BatchNormalization())\n",
        "model_rnn_opt.add(keras.layers.Activation('selu'))\n",
        "\n",
        "model_rnn_opt.add(Dense(2,activation='softmax'))\n",
        "\n",
        "\n",
        "model_rnn_opt.compile(loss='CategoricalCrossentropy',optimizer=keras.optimizers.Adam(learning_rate=0.0022),metrics=[f1_m,precision_m,recall_m])"
      ],
      "metadata": {
        "id": "EL4IcccgYRvv"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_rnn_opt.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WIY1VVeYYRtN",
        "outputId": "95a0d0d5-f601-41f2-f917-d2e38365d675"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " text_vectorization (TextVec  (None, 75)               0         \n",
            " torization)                                                     \n",
            "                                                                 \n",
            " embedding (Embedding)       (None, 75, 128)           1707648   \n",
            "                                                                 \n",
            " global_average_pooling1d_1   (None, 128)              0         \n",
            " (GlobalAveragePooling1D)                                        \n",
            "                                                                 \n",
            " dense_5 (Dense)             (None, 128)               16512     \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 128)               0         \n",
            "                                                                 \n",
            " batch_normalization (BatchN  (None, 128)              512       \n",
            " ormalization)                                                   \n",
            "                                                                 \n",
            " activation (Activation)     (None, 128)               0         \n",
            "                                                                 \n",
            " dense_6 (Dense)             (None, 64)                8256      \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 64)                0         \n",
            "                                                                 \n",
            " batch_normalization_1 (Batc  (None, 64)               256       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " activation_1 (Activation)   (None, 64)                0         \n",
            "                                                                 \n",
            " dense_7 (Dense)             (None, 32)                2080      \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 32)                0         \n",
            "                                                                 \n",
            " batch_normalization_2 (Batc  (None, 32)               128       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " activation_2 (Activation)   (None, 32)                0         \n",
            "                                                                 \n",
            " dense_8 (Dense)             (None, 16)                528       \n",
            "                                                                 \n",
            " dropout_3 (Dropout)         (None, 16)                0         \n",
            "                                                                 \n",
            " batch_normalization_3 (Batc  (None, 16)               64        \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " activation_3 (Activation)   (None, 16)                0         \n",
            "                                                                 \n",
            " dense_9 (Dense)             (None, 2)                 34        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 1,736,018\n",
            "Trainable params: 1,735,538\n",
            "Non-trainable params: 480\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_rnn_hist = model_rnn.fit(train_final,\n",
        "                         epochs=100,\n",
        "                         validation_data=(val_final),\n",
        "                         callbacks=[early_stopping_cb],class_weight = { 0 : 10 , 1 : 150  ,2 : 25})\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PkXICFw2YRqn",
        "outputId": "7adf273c-6484-423a-9867-bb50f94279ad"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "48/48 [==============================] - 1s 13ms/step - loss: 8.6394 - f1_m: 0.4329 - precision_m: 0.4329 - recall_m: 0.4329 - val_loss: 0.7643 - val_f1_m: 0.4201 - val_precision_m: 0.4201 - val_recall_m: 0.4201\n",
            "Epoch 2/100\n",
            "48/48 [==============================] - 0s 9ms/step - loss: 7.2562 - f1_m: 0.4329 - precision_m: 0.4329 - recall_m: 0.4329 - val_loss: 0.7050 - val_f1_m: 0.4201 - val_precision_m: 0.4201 - val_recall_m: 0.4201\n",
            "Epoch 3/100\n",
            "48/48 [==============================] - 0s 6ms/step - loss: 6.9800 - f1_m: 0.4329 - precision_m: 0.4329 - recall_m: 0.4329 - val_loss: 0.6945 - val_f1_m: 0.4201 - val_precision_m: 0.4201 - val_recall_m: 0.4201\n",
            "Epoch 4/100\n",
            "48/48 [==============================] - 0s 7ms/step - loss: 6.9016 - f1_m: 0.5430 - precision_m: 0.5430 - recall_m: 0.5430 - val_loss: 0.6846 - val_f1_m: 0.5799 - val_precision_m: 0.5799 - val_recall_m: 0.5799\n",
            "Epoch 5/100\n",
            "48/48 [==============================] - 0s 7ms/step - loss: 6.8438 - f1_m: 0.5671 - precision_m: 0.5671 - recall_m: 0.5671 - val_loss: 0.6800 - val_f1_m: 0.5799 - val_precision_m: 0.5799 - val_recall_m: 0.5799\n",
            "Epoch 6/100\n",
            "48/48 [==============================] - 0s 7ms/step - loss: 6.8270 - f1_m: 0.5671 - precision_m: 0.5671 - recall_m: 0.5671 - val_loss: 0.6788 - val_f1_m: 0.5799 - val_precision_m: 0.5799 - val_recall_m: 0.5799\n",
            "Epoch 7/100\n",
            "48/48 [==============================] - 0s 7ms/step - loss: 6.8203 - f1_m: 0.5671 - precision_m: 0.5671 - recall_m: 0.5671 - val_loss: 0.6784 - val_f1_m: 0.5799 - val_precision_m: 0.5799 - val_recall_m: 0.5799\n",
            "Epoch 8/100\n",
            "48/48 [==============================] - 0s 7ms/step - loss: 6.8117 - f1_m: 0.5671 - precision_m: 0.5671 - recall_m: 0.5671 - val_loss: 0.6776 - val_f1_m: 0.5799 - val_precision_m: 0.5799 - val_recall_m: 0.5799\n",
            "Epoch 9/100\n",
            "48/48 [==============================] - 0s 7ms/step - loss: 6.8015 - f1_m: 0.5671 - precision_m: 0.5671 - recall_m: 0.5671 - val_loss: 0.6767 - val_f1_m: 0.5799 - val_precision_m: 0.5799 - val_recall_m: 0.5799\n",
            "Epoch 10/100\n",
            "48/48 [==============================] - 0s 7ms/step - loss: 6.7876 - f1_m: 0.5671 - precision_m: 0.5671 - recall_m: 0.5671 - val_loss: 0.6754 - val_f1_m: 0.5799 - val_precision_m: 0.5799 - val_recall_m: 0.5799\n",
            "Epoch 11/100\n",
            "48/48 [==============================] - 0s 7ms/step - loss: 6.7670 - f1_m: 0.5671 - precision_m: 0.5671 - recall_m: 0.5671 - val_loss: 0.6734 - val_f1_m: 0.5799 - val_precision_m: 0.5799 - val_recall_m: 0.5799\n",
            "Epoch 12/100\n",
            "48/48 [==============================] - 0s 7ms/step - loss: 6.7336 - f1_m: 0.5671 - precision_m: 0.5671 - recall_m: 0.5671 - val_loss: 0.6697 - val_f1_m: 0.5799 - val_precision_m: 0.5799 - val_recall_m: 0.5799\n",
            "Epoch 13/100\n",
            "48/48 [==============================] - 1s 10ms/step - loss: 6.6738 - f1_m: 0.5684 - precision_m: 0.5684 - recall_m: 0.5684 - val_loss: 0.6636 - val_f1_m: 0.5799 - val_precision_m: 0.5799 - val_recall_m: 0.5799\n",
            "Epoch 14/100\n",
            "48/48 [==============================] - 1s 15ms/step - loss: 6.5676 - f1_m: 0.5878 - precision_m: 0.5878 - recall_m: 0.5878 - val_loss: 0.6527 - val_f1_m: 0.6063 - val_precision_m: 0.6063 - val_recall_m: 0.6063\n",
            "Epoch 15/100\n",
            "48/48 [==============================] - 0s 9ms/step - loss: 6.3832 - f1_m: 0.6388 - precision_m: 0.6388 - recall_m: 0.6388 - val_loss: 0.6342 - val_f1_m: 0.6407 - val_precision_m: 0.6407 - val_recall_m: 0.6407\n",
            "Epoch 16/100\n",
            "48/48 [==============================] - 0s 8ms/step - loss: 6.0974 - f1_m: 0.6911 - precision_m: 0.6911 - recall_m: 0.6911 - val_loss: 0.6084 - val_f1_m: 0.6733 - val_precision_m: 0.6733 - val_recall_m: 0.6733\n",
            "Epoch 17/100\n",
            "48/48 [==============================] - 0s 7ms/step - loss: 5.6914 - f1_m: 0.7467 - precision_m: 0.7467 - recall_m: 0.7467 - val_loss: 0.5763 - val_f1_m: 0.7220 - val_precision_m: 0.7220 - val_recall_m: 0.7220\n",
            "Epoch 18/100\n",
            "48/48 [==============================] - 0s 8ms/step - loss: 5.2734 - f1_m: 0.7868 - precision_m: 0.7868 - recall_m: 0.7868 - val_loss: 0.5480 - val_f1_m: 0.7684 - val_precision_m: 0.7684 - val_recall_m: 0.7684\n",
            "Epoch 19/100\n",
            "48/48 [==============================] - 0s 7ms/step - loss: 4.8237 - f1_m: 0.8188 - precision_m: 0.8188 - recall_m: 0.8188 - val_loss: 0.5297 - val_f1_m: 0.7685 - val_precision_m: 0.7685 - val_recall_m: 0.7685\n",
            "Epoch 20/100\n",
            "48/48 [==============================] - 0s 6ms/step - loss: 4.4014 - f1_m: 0.8351 - precision_m: 0.8351 - recall_m: 0.8351 - val_loss: 0.5352 - val_f1_m: 0.7505 - val_precision_m: 0.7505 - val_recall_m: 0.7505\n",
            "Epoch 21/100\n",
            "48/48 [==============================] - 0s 6ms/step - loss: 4.0247 - f1_m: 0.8496 - precision_m: 0.8496 - recall_m: 0.8496 - val_loss: 0.5318 - val_f1_m: 0.7461 - val_precision_m: 0.7461 - val_recall_m: 0.7461\n",
            "Epoch 22/100\n",
            "48/48 [==============================] - 0s 6ms/step - loss: 3.7026 - f1_m: 0.8577 - precision_m: 0.8577 - recall_m: 0.8577 - val_loss: 0.5337 - val_f1_m: 0.7454 - val_precision_m: 0.7454 - val_recall_m: 0.7454\n",
            "Epoch 23/100\n",
            "48/48 [==============================] - 0s 6ms/step - loss: 3.4397 - f1_m: 0.8673 - precision_m: 0.8673 - recall_m: 0.8673 - val_loss: 0.5401 - val_f1_m: 0.7460 - val_precision_m: 0.7460 - val_recall_m: 0.7460\n",
            "Epoch 24/100\n",
            "48/48 [==============================] - 0s 6ms/step - loss: 3.2256 - f1_m: 0.8742 - precision_m: 0.8742 - recall_m: 0.8742 - val_loss: 0.5495 - val_f1_m: 0.7419 - val_precision_m: 0.7419 - val_recall_m: 0.7419\n",
            "Epoch 25/100\n",
            "48/48 [==============================] - 0s 6ms/step - loss: 3.0461 - f1_m: 0.8814 - precision_m: 0.8814 - recall_m: 0.8814 - val_loss: 0.5677 - val_f1_m: 0.7381 - val_precision_m: 0.7381 - val_recall_m: 0.7381\n",
            "Epoch 26/100\n",
            "48/48 [==============================] - 0s 6ms/step - loss: 2.8893 - f1_m: 0.8860 - precision_m: 0.8860 - recall_m: 0.8860 - val_loss: 0.5888 - val_f1_m: 0.7314 - val_precision_m: 0.7314 - val_recall_m: 0.7314\n",
            "Epoch 27/100\n",
            "48/48 [==============================] - 0s 6ms/step - loss: 2.7429 - f1_m: 0.8917 - precision_m: 0.8917 - recall_m: 0.8917 - val_loss: 0.6105 - val_f1_m: 0.7277 - val_precision_m: 0.7277 - val_recall_m: 0.7277\n",
            "Epoch 28/100\n",
            "48/48 [==============================] - 0s 6ms/step - loss: 2.6023 - f1_m: 0.8982 - precision_m: 0.8982 - recall_m: 0.8982 - val_loss: 0.6326 - val_f1_m: 0.7224 - val_precision_m: 0.7224 - val_recall_m: 0.7224\n",
            "Epoch 29/100\n",
            "48/48 [==============================] - 0s 6ms/step - loss: 2.4700 - f1_m: 0.9048 - precision_m: 0.9048 - recall_m: 0.9048 - val_loss: 0.6554 - val_f1_m: 0.7198 - val_precision_m: 0.7198 - val_recall_m: 0.7198\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_opt_hist = model_rnn_opt.fit(train_final,\n",
        "                         epochs=10,\n",
        "                         validation_data=(val_final), \n",
        "                         callbacks=[early_stopping_cb],class_weight = { 0:1.5, 1:0.4 ,2:0.5 },verbose=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J6Db_Z5dogwq",
        "outputId": "d067deae-3c47-47fa-bb03-9afc3ee72573"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "48/48 [==============================] - 0s 10ms/step - loss: 0.0639 - f1_m: 0.9862 - precision_m: 0.9862 - recall_m: 0.9862 - val_loss: 2.1899 - val_f1_m: 0.6923 - val_precision_m: 0.6923 - val_recall_m: 0.6923\n",
            "Epoch 2/10\n",
            "48/48 [==============================] - 0s 10ms/step - loss: 0.0636 - f1_m: 0.9855 - precision_m: 0.9855 - recall_m: 0.9855 - val_loss: 2.0881 - val_f1_m: 0.7176 - val_precision_m: 0.7176 - val_recall_m: 0.7176\n",
            "Epoch 3/10\n",
            "48/48 [==============================] - 0s 9ms/step - loss: 0.0667 - f1_m: 0.9840 - precision_m: 0.9840 - recall_m: 0.9840 - val_loss: 1.6820 - val_f1_m: 0.7484 - val_precision_m: 0.7484 - val_recall_m: 0.7484\n",
            "Epoch 4/10\n",
            "48/48 [==============================] - 0s 9ms/step - loss: 0.0576 - f1_m: 0.9869 - precision_m: 0.9869 - recall_m: 0.9869 - val_loss: 1.7920 - val_f1_m: 0.6830 - val_precision_m: 0.6830 - val_recall_m: 0.6830\n",
            "Epoch 5/10\n",
            "48/48 [==============================] - 0s 9ms/step - loss: 0.0653 - f1_m: 0.9851 - precision_m: 0.9851 - recall_m: 0.9851 - val_loss: 2.3357 - val_f1_m: 0.7424 - val_precision_m: 0.7424 - val_recall_m: 0.7424\n",
            "Epoch 6/10\n",
            "48/48 [==============================] - 0s 8ms/step - loss: 0.0731 - f1_m: 0.9840 - precision_m: 0.9840 - recall_m: 0.9840 - val_loss: 1.7655 - val_f1_m: 0.7511 - val_precision_m: 0.7511 - val_recall_m: 0.7511\n",
            "Epoch 7/10\n",
            "48/48 [==============================] - 0s 9ms/step - loss: 0.0660 - f1_m: 0.9857 - precision_m: 0.9857 - recall_m: 0.9857 - val_loss: 1.7501 - val_f1_m: 0.7419 - val_precision_m: 0.7419 - val_recall_m: 0.7419\n",
            "Epoch 8/10\n",
            "48/48 [==============================] - 0s 9ms/step - loss: 0.0650 - f1_m: 0.9860 - precision_m: 0.9860 - recall_m: 0.9860 - val_loss: 1.8473 - val_f1_m: 0.7379 - val_precision_m: 0.7379 - val_recall_m: 0.7379\n",
            "Epoch 9/10\n",
            "48/48 [==============================] - 0s 8ms/step - loss: 0.0645 - f1_m: 0.9862 - precision_m: 0.9862 - recall_m: 0.9862 - val_loss: 1.8963 - val_f1_m: 0.7295 - val_precision_m: 0.7295 - val_recall_m: 0.7295\n",
            "Epoch 10/10\n",
            "48/48 [==============================] - 0s 10ms/step - loss: 0.0698 - f1_m: 0.9865 - precision_m: 0.9865 - recall_m: 0.9865 - val_loss: 2.1551 - val_f1_m: 0.7411 - val_precision_m: 0.7411 - val_recall_m: 0.7411\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# model_rnn.evaluate(x_valid_final,y_valid_final)"
      ],
      "metadata": {
        "id": "EJPQzlmphmrs"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig,ax = plt.subplots(ncols= 2,figsize=(20,8))\n",
        "\n",
        "sns.lineplot(data=pd.DataFrame(model_rnn_hist.history),ax=ax[0])\n",
        "ax[0].set_title(\"Standard RNN\",fontsize= 16)\n",
        "\n",
        "sns.lineplot(data=pd.DataFrame(model_opt_hist.history),ax=ax[1])\n",
        "ax[1].set_title(\"RNN Opptimize\",fontsize= 16)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        },
        "id": "iE-xLzfRpDTu",
        "outputId": "e998301f-50fb-42a8-8cc4-3aaf56776405"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'RNN Opptimize')"
            ]
          },
          "metadata": {},
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x576 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "3he4rEaSYRn_"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "9e4j9b8jov-x"
      },
      "execution_count": 21,
      "outputs": []
    }
  ]
}