News Headlines Dataset For Sarcasm Detection_2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "04048101",
   "metadata": {},
   "source": [
    "**NEWS HEADLINES DATASET FOR SARCASM DETECTION PART 2**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce2a907c",
   "metadata": {},
   "source": [
    "*In this part we will build the module for toidentify sarcastic sentences.*\n",
    "\n",
    "***Plan to work:***\n",
    "\n",
    "        >>> Load and preprocess data  \n",
    "   \n",
    "        >>> Define the Deep Lerning models \n",
    "       \n",
    "        >>> Get the best model to solve the problem \n",
    "       \n",
    "        >>> Save model. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb6d04e0",
   "metadata": {},
   "source": [
    "**Import library necessary**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a5ade9d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import sys\n",
    "import tensorflow as tf\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.callbacks import LearningRateScheduler\n",
    "from keras.callbacks import ModelCheckpoint\n",
    "from keras.callbacks import EarlyStopping\n",
    "from keras.layers import Dropout\n",
    "from keras.layers import BatchNormalization\n",
    "from keras.layers import Activation\n",
    "from keras.layers import Flatten\n",
    "from keras.layers.convolutional import Conv2D, MaxPooling2D, AveragePooling2D\n",
    "from keras.layers import Dense, Dropout, Embedding, SimpleRNN, LSTM, Bidirectional\n",
    "from keras.callbacks import History"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a2b1e626",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import nltk\n",
    "#nltk.download()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26ee32ce",
   "metadata": {},
   "source": [
    "**Reading the Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8e1ecb08",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load the data\n",
    "data = pd.read_json('Sarcasm_Headlines_Dataset_v2.json', lines=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3058af67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>is_sarcastic</th>\n",
       "      <th>headline</th>\n",
       "      <th>article_link</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>thirtysomething scientists unveil doomsday clo...</td>\n",
       "      <td>https://www.theonion.com/thirtysomething-scien...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>dem rep. totally nails why congress is falling...</td>\n",
       "      <td>https://www.huffingtonpost.com/entry/donna-edw...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>eat your veggies: 9 deliciously different recipes</td>\n",
       "      <td>https://www.huffingtonpost.com/entry/eat-your-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>inclement weather prevents liar from getting t...</td>\n",
       "      <td>https://local.theonion.com/inclement-weather-p...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>mother comes pretty close to using word 'strea...</td>\n",
       "      <td>https://www.theonion.com/mother-comes-pretty-c...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>my white inheritance</td>\n",
       "      <td>https://www.huffingtonpost.com/entry/my-white-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>5 ways to file your taxes with less stress</td>\n",
       "      <td>https://www.huffingtonpost.com/entry/5-ways-to...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>richard branson's global-warming donation near...</td>\n",
       "      <td>https://www.theonion.com/richard-bransons-glob...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>shadow government getting too large to meet in...</td>\n",
       "      <td>https://politics.theonion.com/shadow-governmen...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>lots of parents know this scenario</td>\n",
       "      <td>https://www.huffingtonpost.comhttp://pubx.co/6...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   is_sarcastic                                           headline  \\\n",
       "0             1  thirtysomething scientists unveil doomsday clo...   \n",
       "1             0  dem rep. totally nails why congress is falling...   \n",
       "2             0  eat your veggies: 9 deliciously different recipes   \n",
       "3             1  inclement weather prevents liar from getting t...   \n",
       "4             1  mother comes pretty close to using word 'strea...   \n",
       "5             0                               my white inheritance   \n",
       "6             0         5 ways to file your taxes with less stress   \n",
       "7             1  richard branson's global-warming donation near...   \n",
       "8             1  shadow government getting too large to meet in...   \n",
       "9             0                 lots of parents know this scenario   \n",
       "\n",
       "                                        article_link  \n",
       "0  https://www.theonion.com/thirtysomething-scien...  \n",
       "1  https://www.huffingtonpost.com/entry/donna-edw...  \n",
       "2  https://www.huffingtonpost.com/entry/eat-your-...  \n",
       "3  https://local.theonion.com/inclement-weather-p...  \n",
       "4  https://www.theonion.com/mother-comes-pretty-c...  \n",
       "5  https://www.huffingtonpost.com/entry/my-white-...  \n",
       "6  https://www.huffingtonpost.com/entry/5-ways-to...  \n",
       "7  https://www.theonion.com/richard-bransons-glob...  \n",
       "8  https://politics.theonion.com/shadow-governmen...  \n",
       "9  https://www.huffingtonpost.comhttp://pubx.co/6...  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# view the data\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dafe91ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We have 287674 words in the headline\n"
     ]
    }
   ],
   "source": [
    "data_len = data['headline'].apply(lambda x: len(x.split(' '))).sum()\n",
    "print(f'We have {data_len} words in the headline')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aef967d6",
   "metadata": {},
   "source": [
    "***Text pre-processing***\n",
    "\n",
    "For the text cleaning we can use a some libreries as nltk and spacy. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a0438285",
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.tokenize import word_tokenize, sent_tokenize\n",
    "from nltk.corpus import stopwords\n",
    "import spacy\n",
    "import string\n",
    "import re\n",
    "\n",
    "nlp = spacy.load('en_core_web_sm')  #load spacy\n",
    "stop_words = set(stopwords.words('english')) # get the english stropwords\n",
    "def text_clean(text):                        #define function for clean the data  \n",
    "    doc = nlp(text)\n",
    "    lem_tok = [token.lemma_ for token in doc] # tokenize the words using spacy\n",
    "    lem_al = [lemma.lower() for lemma in lem_tok if lemma.isalpha() and lemma not in stop_words]\n",
    "    lem_text = ' '.join(lem_al)\n",
    "    return lem_text\n",
    "\n",
    "data['headline'] = data['headline'].apply(text_clean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2ba6f143",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " After data cleaning we have 204866 words in the headline\n"
     ]
    }
   ],
   "source": [
    "data_len = data['headline'].apply(lambda x: len(x.split(' '))).sum()\n",
    "print(f' After data cleaning we have {data_len} words in the headline')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5f63487a",
   "metadata": {},
   "outputs": [],
   "source": [
    "text = data['headline'].tolist()\n",
    "labels = data['is_sarcastic'].tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b9f6d74",
   "metadata": {},
   "source": [
    "*Split, tokenize and padding the text with keras*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "364d1491",
   "metadata": {},
   "outputs": [],
   "source": [
    "max_words = 10000 # max number of words in \n",
    "max_length = 100 # max length\n",
    "trunc_type='post' # types of padding\n",
    "padding_type='post'\n",
    "oov_tok = \"<OOV>\"  # out-of-vocab\n",
    "training_size = 20000 # size of training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5782c1d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# split the dataset into training and testing datasets \n",
    "x_train = text[0:training_size]\n",
    "x_test = text[training_size:]\n",
    "y_train  = labels[0:training_size]\n",
    "y_test  = labels[training_size:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5b316451",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import library for tokenize text\n",
    "from tensorflow.keras.preprocessing.text import Tokenizer\n",
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "\n",
    "token = Tokenizer(num_words=max_words, oov_token=oov_tok)\n",
    "token.fit_on_texts(x_train)\n",
    "word_index = token.word_index\n",
    "\n",
    "# Transforming train_text to a sequence of integers\n",
    "train_seq = token.texts_to_sequences(x_train)             \n",
    "\n",
    "# Pading train_sequences to the same length\n",
    "train_pad = pad_sequences(train_seq, maxlen=max_length, padding=padding_type, truncating=trunc_type) \n",
    "\n",
    "# Transforming test_text to a sequence of integers\n",
    "test_seq = token.texts_to_sequences(x_test) \n",
    "\n",
    "# Pading test_sequences to the same length\n",
    "test_pad = pad_sequences(test_seq, maxlen=max_length, padding=padding_type, truncating=trunc_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c8dcbdf2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "int32\n"
     ]
    }
   ],
   "source": [
    "# conwert the data into numpy array\n",
    "x_train = np.array(train_pad)\n",
    "y_train = np.array(y_train)\n",
    "x_test  = np.array(test_pad)\n",
    "y_test  = np.array(y_test)\n",
    "\n",
    "x_train.shape, y_train.shape, x_test.shape, y_test.shape #check the shapes\n",
    "print(x_train.dtype) # check the type of train values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdad56f0",
   "metadata": {},
   "source": [
    "***Modeling the data***\n",
    "\n",
    "*TensorFlow*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1da4bf03",
   "metadata": {},
   "source": [
    "Using Dense, Embedding and GlobalAveragePooling1D layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e76cec19",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " embedding (Embedding)       (None, 100, 16)           160000    \n",
      "                                                                 \n",
      " global_average_pooling1d (G  (None, 16)               0         \n",
      " lobalAveragePooling1D)                                          \n",
      "                                                                 \n",
      " dense (Dense)               (None, 16)                272       \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 1)                 17        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 160,289\n",
      "Trainable params: 160,289\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "INFO:tensorflow:Assets written to: /models/model_1\\assets\n",
      "Epoch 1/30\n",
      "625/625 - 2s - loss: 0.6923 - accuracy: 0.5213 - val_loss: 0.6918 - val_accuracy: 0.5249 - lr: 0.0100 - 2s/epoch - 3ms/step\n",
      "Epoch 2/30\n",
      "625/625 - 1s - loss: 0.6920 - accuracy: 0.5231 - val_loss: 0.6918 - val_accuracy: 0.5249 - lr: 0.0100 - 1s/epoch - 2ms/step\n",
      "Epoch 3/30\n",
      "625/625 - 1s - loss: 0.6920 - accuracy: 0.5231 - val_loss: 0.6917 - val_accuracy: 0.5249 - lr: 0.0100 - 1s/epoch - 2ms/step\n",
      "Epoch 4/30\n",
      "625/625 - 1s - loss: 0.6920 - accuracy: 0.5231 - val_loss: 0.6917 - val_accuracy: 0.5249 - lr: 0.0100 - 1s/epoch - 2ms/step\n",
      "Epoch 5/30\n",
      "625/625 - 1s - loss: 0.6920 - accuracy: 0.5231 - val_loss: 0.6917 - val_accuracy: 0.5249 - lr: 0.0100 - 1s/epoch - 2ms/step\n",
      "Epoch 6/30\n",
      "625/625 - 1s - loss: 0.6919 - accuracy: 0.5231 - val_loss: 0.6916 - val_accuracy: 0.5249 - lr: 0.0100 - 898ms/epoch - 1ms/step\n",
      "Epoch 7/30\n",
      "625/625 - 1s - loss: 0.6918 - accuracy: 0.5231 - val_loss: 0.6918 - val_accuracy: 0.5249 - lr: 0.0100 - 1s/epoch - 2ms/step\n",
      "Epoch 8/30\n",
      "625/625 - 1s - loss: 0.6918 - accuracy: 0.5231 - val_loss: 0.6917 - val_accuracy: 0.5249 - lr: 0.0100 - 1s/epoch - 2ms/step\n",
      "Epoch 9/30\n",
      "625/625 - 1s - loss: 0.6918 - accuracy: 0.5231 - val_loss: 0.6914 - val_accuracy: 0.5249 - lr: 0.0100 - 1s/epoch - 2ms/step\n",
      "Epoch 10/30\n",
      "625/625 - 1s - loss: 0.6917 - accuracy: 0.5231 - val_loss: 0.6914 - val_accuracy: 0.5249 - lr: 0.0100 - 1s/epoch - 2ms/step\n",
      "Epoch 11/30\n",
      "625/625 - 1s - loss: 0.6917 - accuracy: 0.5231 - val_loss: 0.6913 - val_accuracy: 0.5249 - lr: 0.0090 - 1s/epoch - 2ms/step\n",
      "Epoch 12/30\n",
      "625/625 - 1s - loss: 0.6916 - accuracy: 0.5231 - val_loss: 0.6914 - val_accuracy: 0.5249 - lr: 0.0082 - 1s/epoch - 2ms/step\n",
      "Epoch 13/30\n",
      "625/625 - 1s - loss: 0.6916 - accuracy: 0.5231 - val_loss: 0.6913 - val_accuracy: 0.5249 - lr: 0.0074 - 1s/epoch - 2ms/step\n",
      "Epoch 14/30\n",
      "625/625 - 1s - loss: 0.6916 - accuracy: 0.5231 - val_loss: 0.6913 - val_accuracy: 0.5249 - lr: 0.0067 - 1s/epoch - 2ms/step\n",
      "Epoch 15/30\n",
      "625/625 - 1s - loss: 0.6915 - accuracy: 0.5231 - val_loss: 0.6912 - val_accuracy: 0.5249 - lr: 0.0061 - 1s/epoch - 2ms/step\n",
      "Epoch 16/30\n",
      "625/625 - 1s - loss: 0.6915 - accuracy: 0.5231 - val_loss: 0.6912 - val_accuracy: 0.5249 - lr: 0.0055 - 1s/epoch - 2ms/step\n",
      "Epoch 17/30\n",
      "625/625 - 1s - loss: 0.6914 - accuracy: 0.5231 - val_loss: 0.6913 - val_accuracy: 0.5249 - lr: 0.0050 - 1s/epoch - 2ms/step\n",
      "Epoch 18/30\n",
      "625/625 - 1s - loss: 0.6914 - accuracy: 0.5231 - val_loss: 0.6912 - val_accuracy: 0.5249 - lr: 0.0045 - 1s/epoch - 2ms/step\n",
      "Epoch 19/30\n",
      "625/625 - 1s - loss: 0.6914 - accuracy: 0.5231 - val_loss: 0.6911 - val_accuracy: 0.5249 - lr: 0.0041 - 1s/epoch - 2ms/step\n",
      "Epoch 20/30\n",
      "625/625 - 1s - loss: 0.6913 - accuracy: 0.5231 - val_loss: 0.6911 - val_accuracy: 0.5249 - lr: 0.0037 - 1s/epoch - 2ms/step\n",
      "Epoch 21/30\n",
      "625/625 - 1s - loss: 0.6913 - accuracy: 0.5231 - val_loss: 0.6911 - val_accuracy: 0.5249 - lr: 0.0033 - 1s/epoch - 2ms/step\n",
      "Epoch 22/30\n",
      "625/625 - 1s - loss: 0.6913 - accuracy: 0.5231 - val_loss: 0.6911 - val_accuracy: 0.5249 - lr: 0.0030 - 1s/epoch - 2ms/step\n",
      "Epoch 23/30\n",
      "625/625 - 1s - loss: 0.6913 - accuracy: 0.5231 - val_loss: 0.6910 - val_accuracy: 0.5249 - lr: 0.0027 - 1s/epoch - 2ms/step\n",
      "Epoch 24/30\n",
      "625/625 - 1s - loss: 0.6912 - accuracy: 0.5231 - val_loss: 0.6911 - val_accuracy: 0.5249 - lr: 0.0025 - 1s/epoch - 2ms/step\n",
      "Epoch 25/30\n",
      "625/625 - 1s - loss: 0.6912 - accuracy: 0.5231 - val_loss: 0.6910 - val_accuracy: 0.5249 - lr: 0.0022 - 1s/epoch - 2ms/step\n",
      "Epoch 26/30\n",
      "625/625 - 1s - loss: 0.6912 - accuracy: 0.5231 - val_loss: 0.6910 - val_accuracy: 0.5249 - lr: 0.0020 - 1s/epoch - 2ms/step\n",
      "Epoch 27/30\n",
      "625/625 - 1s - loss: 0.6912 - accuracy: 0.5231 - val_loss: 0.6910 - val_accuracy: 0.5249 - lr: 0.0018 - 1s/epoch - 2ms/step\n",
      "Epoch 28/30\n",
      "625/625 - 1s - loss: 0.6912 - accuracy: 0.5231 - val_loss: 0.6910 - val_accuracy: 0.5249 - lr: 0.0017 - 1s/epoch - 2ms/step\n",
      "Epoch 29/30\n",
      "625/625 - 1s - loss: 0.6912 - accuracy: 0.5231 - val_loss: 0.6910 - val_accuracy: 0.5249 - lr: 0.0015 - 1s/epoch - 2ms/step\n",
      "Epoch 30/30\n",
      "625/625 - 1s - loss: 0.6912 - accuracy: 0.5231 - val_loss: 0.6910 - val_accuracy: 0.5249 - lr: 0.0014 - 1s/epoch - 2ms/step\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense,Embedding, GlobalAveragePooling1D\n",
    "from keras.callbacks import LearningRateScheduler, ModelCheckpoint\n",
    "\n",
    "# define function for lerning rate schedule \n",
    "def scheduler(epoch, lr): \n",
    "      if epoch < 10:\n",
    "        return lr\n",
    "      else:\n",
    "        return lr * tf.math.exp(-0.1)\n",
    "\n",
    "# max number of words to use into model\n",
    "max_words = 10000\n",
    "# max embedding number\n",
    "embedding_dim = 16\n",
    "# number epochs to use\n",
    "max_epochs = 30 \n",
    "# Add Embedding and GlobalAveragePooling1D layers in model\n",
    "model_1 = Sequential() \n",
    "model_1.add(Embedding(max_words, embedding_dim, input_length = max_length))\n",
    "model_1.add(GlobalAveragePooling1D())\n",
    "model_1.add(Dense(16, activation='relu'))\n",
    "model_1.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "#compile the model \n",
    "model_1.compile(loss='binary_crossentropy',optimizer='sgd',metrics=['accuracy'])\n",
    "\n",
    "#summarize the model\n",
    "model_1.summary()\n",
    "\n",
    "#saving the model\n",
    "save_model1 = model_1.save(\"/models/model_1\")\n",
    "\n",
    "#use early_stopping\n",
    "early_stopping = EarlyStopping(monitor=\"val_loss\", \n",
    "                               patience=3, \n",
    "                               restore_best_weights=True)\n",
    "# learning rate\n",
    "l_rate = LearningRateScheduler(scheduler)\n",
    "# fit the model\n",
    "history_1 = model_1.fit(x_train, y_train, \n",
    "                    epochs=max_epochs,\n",
    "                    batch_size=32,\n",
    "                    callbacks=[l_rate, early_stopping], \n",
    "                    validation_data=(x_test, y_test), \n",
    "                    verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a0f223b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train accuracy for GlobalAv1: 52.31\n",
      "Test accuracy for GlobalAv1: 52.49\n"
     ]
    }
   ],
   "source": [
    "# model evaluation\n",
    "train_GlobalAv1 = model_1.evaluate(x_train, y_train, verbose=0, batch_size=32)\n",
    "test_GlobalAv1 = model_1.evaluate(x_test, y_test, verbose=0, batch_size=32)\n",
    "print(f'Train accuracy for GlobalAv1: {train_GlobalAv1[1]*100:0.2f}')\n",
    "print(f'Test accuracy for GlobalAv1: {test_GlobalAv1[1]*100:0.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "92341b05",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.4753083 ]\n",
      " [0.47755906]]\n"
     ]
    }
   ],
   "source": [
    "#check the model\n",
    "sentences = ['lesbian consider father indiana amazing one', \"terrifying server whole heartedly care guest dining experience\"]\n",
    "sequencess = token.texts_to_sequences(sentences)\n",
    "\n",
    "padded = pad_sequences(sequencess, maxlen=max_length, padding=padding_type, truncating=trunc_type)\n",
    "\n",
    "# make a prediction\n",
    "pred_1 = model_1.predict(padded)\n",
    "print(pred_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6b91bee9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot the accuracy\n",
    "plt.plot(history_1.history['accuracy'], label = \"accuracy\")\n",
    "plt.plot(history_1.history['val_accuracy'], label = \"val_accuracy\")\n",
    "\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "plt.plot(history_1.history['loss'], label = \"loss\")\n",
    "plt.plot(history_1.history['val_loss'], label = \"val_loss\")\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "73cd5255",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " embedding_1 (Embedding)     (None, 100, 16)           160000    \n",
      "                                                                 \n",
      " global_average_pooling1d_1   (None, 16)               0         \n",
      " (GlobalAveragePooling1D)                                        \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 150)               2550      \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 100)               15100     \n",
      "                                                                 \n",
      " dense_4 (Dense)             (None, 50)                5050      \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 50)                0         \n",
      "                                                                 \n",
      " dense_5 (Dense)             (None, 1)                 51        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 182,751\n",
      "Trainable params: 182,751\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "INFO:tensorflow:Assets written to: /models/model_2\\assets\n",
      "Epoch 1/50\n",
      "625/625 - 2s - loss: 0.7007 - accuracy: 0.5235 - val_loss: 0.6395 - val_accuracy: 0.6817 - lr: 0.0010 - 2s/epoch - 3ms/step\n",
      "Epoch 2/50\n",
      "625/625 - 1s - loss: 0.4616 - accuracy: 0.7842 - val_loss: 0.4387 - val_accuracy: 0.7984 - lr: 0.0010 - 1s/epoch - 2ms/step\n",
      "Epoch 3/50\n",
      "625/625 - 1s - loss: 0.3517 - accuracy: 0.8464 - val_loss: 0.4528 - val_accuracy: 0.7936 - lr: 0.0010 - 1s/epoch - 2ms/step\n",
      "Epoch 4/50\n",
      "625/625 - 1s - loss: 0.3049 - accuracy: 0.8713 - val_loss: 0.4637 - val_accuracy: 0.7972 - lr: 0.0010 - 1s/epoch - 2ms/step\n",
      "Epoch 5/50\n",
      "625/625 - 1s - loss: 0.2681 - accuracy: 0.8907 - val_loss: 0.4907 - val_accuracy: 0.7936 - lr: 0.0010 - 1s/epoch - 2ms/step\n",
      "Epoch 6/50\n",
      "625/625 - 1s - loss: 0.2417 - accuracy: 0.9039 - val_loss: 0.5274 - val_accuracy: 0.7874 - lr: 0.0010 - 1s/epoch - 2ms/step\n",
      "Epoch 7/50\n",
      "625/625 - 1s - loss: 0.2246 - accuracy: 0.9117 - val_loss: 0.5277 - val_accuracy: 0.7898 - lr: 0.0010 - 1s/epoch - 2ms/step\n",
      "Epoch 8/50\n",
      "625/625 - 1s - loss: 0.2078 - accuracy: 0.9200 - val_loss: 0.5693 - val_accuracy: 0.7819 - lr: 0.0010 - 1s/epoch - 2ms/step\n",
      "Epoch 9/50\n",
      "625/625 - 1s - loss: 0.1930 - accuracy: 0.9274 - val_loss: 0.6042 - val_accuracy: 0.7794 - lr: 0.0010 - 1s/epoch - 2ms/step\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, GlobalAveragePooling1D\n",
    "\n",
    "# define function for lerning rate schedule \n",
    "def scheduler(epoch, lr): \n",
    "      if epoch < 10:\n",
    "        return lr\n",
    "      else:\n",
    "        return lr * tf.math.exp(-0.1)\n",
    "    \n",
    "max_epochs = 50 #number epochs to use\n",
    "\n",
    "model_2 = Sequential()\n",
    "model_2.add(Embedding(max_words, embedding_dim, input_length = max_length))\n",
    "model_2.add(GlobalAveragePooling1D())\n",
    "model_2.add(Dense(150, activation='relu'))\n",
    "model_2.add(Dense(100, activation='relu'))\n",
    "model_2.add(Dense(50, activation='sigmoid'))\n",
    "model_2.add(Dropout(.2))\n",
    "model_2.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "model_2.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])\n",
    "model_2.summary()\n",
    "\n",
    "save_model2 = model_2.save(\"/models/model_2\") # saving model\n",
    "\n",
    "#use early_stopping\n",
    "early_stopping = EarlyStopping(monitor=\"val_loss\", \n",
    "                               patience=7, \n",
    "                               restore_best_weights=True)\n",
    "# learning rate\n",
    "l_rate = LearningRateScheduler(scheduler)\n",
    "\n",
    "history_2 = model_2.fit(x_train, y_train, \n",
    "                    epochs=max_epochs,\n",
    "                    batch_size=32,\n",
    "                    callbacks=[l_rate,early_stopping],    \n",
    "                    validation_data=(x_test, y_test),\n",
    "                    verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "098b9636",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train accuracy for train_GlobalAv2: 85.90\n",
      "Test accuracy for test_GlobalAv2: 79.84\n"
     ]
    }
   ],
   "source": [
    "# model evaluation\n",
    "train_GlobalAv2 = model_2.evaluate(x_train, y_train, verbose=0, batch_size=32)\n",
    "test_GlobalAv2 = model_2.evaluate(x_test, y_test, verbose=0, batch_size=32)\n",
    "print(f'Train accuracy for train_GlobalAv2: {train_GlobalAv2[1]*100:0.2f}')\n",
    "print(f'Test accuracy for test_GlobalAv2: {test_GlobalAv2[1]*100:0.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1310343d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.23646468]\n",
      " [0.93241876]]\n"
     ]
    }
   ],
   "source": [
    "#check the model\n",
    "sentences = ['lesbian consider father indiana amazing one', \"terrifying server whole heartedly care guest dining experience\"]\n",
    "sequencess = token.texts_to_sequences(sentences)\n",
    "\n",
    "padded = pad_sequences(sequencess, maxlen=max_length, padding=padding_type, truncating=trunc_type)\n",
    "\n",
    "# make a prediction\n",
    "pred_2 = model_2.predict(padded)\n",
    "print(pred_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ac3fd7da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history_2.history['accuracy'], label = \"acc_mod_2\")\n",
    "plt.plot(history_2.history['val_accuracy'], label = \"val_acc_mod_2\")\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "plt.plot(history_2.history['loss'], label = \"loss\")\n",
    "plt.plot(history_2.history['val_loss'], label = \"val_loss\")\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbc4627e",
   "metadata": {},
   "source": [
    "Using the GlobalAveragePooling1D & Convolution1D layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3fbbf8ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " embedding_2 (Embedding)     (None, 100, 16)           160000    \n",
      "                                                                 \n",
      " conv1d (Conv1D)             (None, 98, 200)           9800      \n",
      "                                                                 \n",
      " global_average_pooling1d_2   (None, 200)              0         \n",
      " (GlobalAveragePooling1D)                                        \n",
      "                                                                 \n",
      " dense_6 (Dense)             (None, 150)               30150     \n",
      "                                                                 \n",
      " dense_7 (Dense)             (None, 100)               15100     \n",
      "                                                                 \n",
      " dense_8 (Dense)             (None, 50)                5050      \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 50)                0         \n",
      "                                                                 \n",
      " dense_9 (Dense)             (None, 1)                 51        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 220,151\n",
      "Trainable params: 220,151\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "INFO:tensorflow:Assets written to: /models/model_3\\assets\n",
      "Epoch 1/100\n",
      "625/625 - 4s - loss: 0.6099 - accuracy: 0.6331 - val_loss: 0.4839 - val_accuracy: 0.7680 - lr: 0.0010 - 4s/epoch - 6ms/step\n",
      "Epoch 2/100\n",
      "625/625 - 3s - loss: 0.3963 - accuracy: 0.8219 - val_loss: 0.4719 - val_accuracy: 0.7771 - lr: 0.0010 - 3s/epoch - 5ms/step\n",
      "Epoch 3/100\n",
      "625/625 - 3s - loss: 0.2907 - accuracy: 0.8788 - val_loss: 0.4540 - val_accuracy: 0.7988 - lr: 0.0010 - 3s/epoch - 5ms/step\n",
      "Epoch 4/100\n",
      "625/625 - 3s - loss: 0.2170 - accuracy: 0.9182 - val_loss: 0.5266 - val_accuracy: 0.7914 - lr: 0.0010 - 3s/epoch - 6ms/step\n",
      "Epoch 5/100\n",
      "625/625 - 4s - loss: 0.1618 - accuracy: 0.9446 - val_loss: 0.6042 - val_accuracy: 0.7881 - lr: 0.0010 - 4s/epoch - 6ms/step\n",
      "Epoch 6/100\n",
      "625/625 - 4s - loss: 0.1114 - accuracy: 0.9650 - val_loss: 0.6721 - val_accuracy: 0.7840 - lr: 0.0010 - 4s/epoch - 6ms/step\n",
      "Epoch 7/100\n",
      "625/625 - 4s - loss: 0.0780 - accuracy: 0.9783 - val_loss: 0.7577 - val_accuracy: 0.7828 - lr: 0.0010 - 4s/epoch - 6ms/step\n",
      "Epoch 8/100\n",
      "625/625 - 4s - loss: 0.0576 - accuracy: 0.9854 - val_loss: 0.8630 - val_accuracy: 0.7782 - lr: 0.0010 - 4s/epoch - 6ms/step\n",
      "Epoch 9/100\n",
      "625/625 - 4s - loss: 0.0457 - accuracy: 0.9882 - val_loss: 0.9072 - val_accuracy: 0.7758 - lr: 0.0010 - 4s/epoch - 7ms/step\n",
      "Epoch 10/100\n",
      "625/625 - 4s - loss: 0.0379 - accuracy: 0.9903 - val_loss: 0.9989 - val_accuracy: 0.7769 - lr: 0.0010 - 4s/epoch - 6ms/step\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, GlobalAveragePooling1D, Convolution1D\n",
    "from keras.callbacks import LearningRateScheduler, ModelCheckpoint\n",
    "\n",
    "# define function for lerning rate schedule \n",
    "def scheduler(epoch, lr): \n",
    "      if epoch < 10:\n",
    "        return lr\n",
    "      else:\n",
    "        return lr * tf.math.exp(-0.1)\n",
    "max_epochs = 100 # number epochs to use\n",
    "\n",
    "model_3 = Sequential()  # define the model\n",
    "model_3.add(Embedding(max_words, embedding_dim, input_length = max_length))\n",
    "model_3.add(Convolution1D(200,3, activation='relu'))  # add the convolutional Layer\n",
    "model_3.add(GlobalAveragePooling1D())                 # add the average polling\n",
    "model_3.add(Dense(150, activation='relu'))\n",
    "model_3.add(Dense(100, activation='relu'))             # add the dense layers\n",
    "model_3.add(Dense(50, activation='sigmoid'))\n",
    "model_3.add(Dropout(.1))\n",
    "model_3.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "model_3.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy']) # compile the model\n",
    "\n",
    "model_3.summary() # model summarize\n",
    "\n",
    "save_model3 = model_3.save(\"/models/model_3\") # saving model\n",
    "\n",
    "#use early_stopping\n",
    "early_stopping = EarlyStopping(monitor=\"val_loss\", \n",
    "                               patience=7, \n",
    "                               restore_best_weights=True)\n",
    "# learning rate\n",
    "l_rate = LearningRateScheduler(scheduler)\n",
    "\n",
    "# fit the model\n",
    "history_3 = model_3.fit(x_train, y_train, \n",
    "                    epochs=max_epochs,\n",
    "                    batch_size=32,\n",
    "                    callbacks=[early_stopping,l_rate],\n",
    "                    validation_data=(x_test, y_test),\n",
    "                    verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a98d5108",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train accuracy for Conv: 93.59\n",
      "Test accuracy for  Conv: 79.88\n"
     ]
    }
   ],
   "source": [
    "# model evaluation\n",
    "train_conv_results =model_3.evaluate(x_train, y_train, verbose=0, batch_size=32)\n",
    "test_conv_results = model_3.evaluate(x_test, y_test, verbose=0, batch_size=32)\n",
    "print(f'Train accuracy for Conv: {train_conv_results[1]*100:0.2f}')\n",
    "print(f'Test accuracy for  Conv: {test_conv_results[1]*100:0.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "086666b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.03546032]\n",
      " [0.9841269 ]]\n"
     ]
    }
   ],
   "source": [
    "#check the model\n",
    "sentences = ['lesbian consider father indiana amazing one', \"terrifying server whole heartedly care guest dining experience\"]\n",
    "sequencess = token.texts_to_sequences(sentences)\n",
    "\n",
    "padded = pad_sequences(sequencess, maxlen=max_length, padding=padding_type, truncating=trunc_type)\n",
    "\n",
    "# make a prediction\n",
    "pred_3 = model_3.predict(padded)\n",
    "print(pred_3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4485743e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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NkgaBUuqk/MOw5G+wcgYERsAtb0G3W3RgmIfTIFBKWeMAlr1qLRtZXmKNCr76dxDS1OnKVD3QIFDKm5Ucg+XT4fu/Q0me9e3/yt/q/EBeRoNAKW9UVgyr3oIlL0FhFnQaBlc+AS27OV2ZcoAGgVLepKIM1r4Hi1+AvHRrlbCrnoK4ZKcrUw7SIFDKG1RWwMa5sPAvkL0b4nrDTdOtqaKV19MgUMqTGWOtEPa/ZyBzC7RItFYK6zhErwRSJ2gQKOWJjIFd/4P//Rky1kDTjnDrO9D1RvCxdWFC1QhpECjlafYtt0YD710KEfEwchokjQZf/XNXNdP/GUp5igPrrTOAHd9ASHO47gW45E6dGE6dlQaBUo1d5jZY+Axs/hQCI2Hw76H3BPAPcboy1UhoECjVWGXvge+eg5QPwBVsTQ3db5I1NYRS50CDQKnGJu8ALHkRVv/Lmgm07/0wcAqENHO6MtVIaRAo1VgUHoWlU62poSvLodfPYdAjEN7K6cpUI6dBoFRDV5wHy/8B378GpfnQfbTVDNSkrdOVKQ+hQaBUQ1VaCCvfhKUvQ9FR6HKDNR9Q885OV6Y8jAaBUg1NeSms+RcsfhHyD0KHwXDVk9Cqp9OVKQ+lQaBUQ1Feai0Qv+iv1jrB8f3hZ+9Am/5OV6Y8nAaBUk7LTYPV71pXARUchpge1tKQ7a/W+YBUvdAgUMoJlZWQuhBWvgXbv7TmBuo4xFocvuM1GgCqXmkQKFWfCo/CullWAGTvhuBmMGAyXDIeoto4XZ3yUhoEStnNGEhfbX34b5wLFSUQ38+6AqjrDToXkHKcBoFSdiktgA1zrCUhD6wH/1BrUfhL74YWFztdnVInaBAoVdcyt8Oqt2Hd+1CSC827wvC/QdIoCAhzujqlfkKDQKm6UFEGW7+wvv3vXgw+Lug60vr2H99PO39Vg6ZBoNSFyE23Bn+t/pc1+CuiNVz9NPQcB6HNna5OqVrRIFDqXFVWwu5F1rf/rfPAVFqjfy99xbr008fX6QqVOicaBErVVlG21e6/6m04shOCmkD/B6xLP3UCONWIaRAodTbpa6xv/xvmQnkRxPWGm96w+gBcgU5Xp9QF0yBQqialhbDpI+va/4w11gpg3UdB8t0Qk+R0dUrVKVuDQESuBV4BfIEZxpi/1rDPFcDLgAvIMsZcbkcth48V88aiVB69rjMuXx87XkJ5gqyd7ks/Z0FxDjTrZC0C332ULgGpPJZtQSAivsA04BogDVgpIp8ZYzZX2ScS+AdwrTFmn4jYdpnFqj3ZzFi6G4AnR3S162VUY1RZCdvmwcoZ1vw/Pn7Q5Xpr3p82A/TST+Xx7Dwj6A3sNMakAojIB8BIYHOVfe4APjLG7AMwxhy2q5hhiTHc2a8NM5buplebKIYlxtj1UqqxKC+FDR9aC78c2QHhcXDlk9YSkGEtnK5OqXpjZxDEAvur3E8D+lTb5yLAJSLfAWHAK8aYmdUPJCITgAkA8fHx513QE8O7sj4tl9/MSaFzyzDaRYee97FUI1ZaAGtmWks/5qVBi0S49W3oMhJ8tdtMeR87G8trOp821e77AZcAw4GhwFMictFPnmTMG8aYZGNMcnR09HkX5O/nw7QxvXD5Cve9t4bC0vLzPpZqhIqyYdHzMLUbfPUYRMbDmDkwcQl0u0VDQHktO4MgDWhd5X4ckFHDPl8ZYwqMMVnAYqC7jTURGxnEK6N7sv3wMZ78eCPGVM8m5XHyDsA3T1oBsPAZaN0b7voa7vpS5/5XCnubhlYCHUWkLZAOjMbqE6jqU+A1EfED/LGajqbaWBMAgy6KZvLVHXl5wQ4uSYhiTB+dB94jHdkFy16B9f+GynLrW/+Ah6FlN6crU6pBsS0IjDHlIvIA8DXW5aNvG2M2ichE9+PTjTFbROQrIAWoxLrEdKNdNVX10FUdWbsvhz98tpnE2AiS4iLr42VVfTiQAkunwuZPrMnfeo6F/g/p6F+lTkMaW9NIcnKyWbVqVZ0cK7uglBF/XwrAFw8NJDLYv06Oqxyy93tY8hLsnA/+YXDpXdD3fghr6XRlSjlORFYbY5JresyrR1ZFhfgzbUwvDh8rZsrsdVRWNq5QVFirf23/Gt4aCu9cZ40CvupJmLIRrvmjhoBSteDVQQDQo3UkT4/oysJtmfzju51Ol6Nqq6LcWv1r+kB4/zbIS4frnoeHN8KgRyAo0ukKlWo09Ho5YGzfNqzam81L87fTMz6KAR2aOV2SOp2yYlj/vtUJnL3HmgLixumQeCv4upyuTqlGqVZnBCISIiI+7tsXicgNIuIxf3UiwrM3J9I+OpSH/r2Wg7nFTpekqis5Zn34v5IE/50CwU1h1Cy4fzn0uF1DQKkLUNumocVAoIjEAt8C44F37SrKCcH+frw+9hKKyyqY9P4ayioqnS5JARRkwf/+DFMvhvlPQ/Mu8PPP4J5vocsI8PH61k2lLlht/4rEGFMI3Az83RhzE+BxM7d1aB7Kc7cmsXpvNs/O2+p0Od4tZz98+ag1CGzxi9B2ENz7P/j5p9Duch0EplQdqm0fgYhIP2AMcPc5PrdRGZHUilV7snl72W4uaRPF8CSdnK5eZW6HZS9DymzrftIoGDAZojs5WpZSnqy2H+YPA48DH7sHhbUDFtpWlcN+O6wLKWk5/GbOejrHhNFeJ6ezX/oaWPoSbPkv+AVaU0D3ewAiW5/9uUqpC3LOA8rcncahxpg8e0o6s7ocUHYmB3KLGP7qUpqF+vPJpAEE+3vkCZCzjLEWgV86FVK/sxZ+6T0B+kyEEL1yS6m6dMEDykTkfREJF5EQrPUEtonII3VZZEMTExHEK6N7sONwPk/o5HR1q6wY1r5njQGYORIOb7EGfz280RoMpiGgVL2qbWdxV/cZwI3APCAeGGdXUQ3FZR2jmTL4Ij5em86sFfucLqfxy8+E7/4KL3eDTydZZwQjp8HkFKsfIDDc6QqV8kq1be9wuccN3Ai8ZowpExGv+Ir8wJUdWLMvmz9+vpmkOJ2c7rwc2gTL/wEp/4GKEug4FPrdD2316h+lGoLanhH8E9gDhACLRaQN4EgfQX3z8RGm3taD6LAA7ntvDdkFpU6X1DhUVsL2b6ymn9f7w4a51iygD6yCMR9Cuys0BJRqIM579lER8TPG1PsSX/XVWVzd+v05/Gz6D/Tv0JS377wUHx/9EKtRaaE1//+K6ZC1HcJaQe974ZJfQHATp6tTymudqbO4Vk1DIhIB/A4Y5N60CPgjkFsnFTYC3VtH8tT1XXnqk41MW7iTB6/u6HRJDUteBvz4Jqx+x1oSslVPuHkGXHyjTv+gVANX2z6Ct4GNwG3u++OAd7BGGnuNsX3iWb3nKC8tsCanG9hRr24hYy388A/Y9BGYSug8HPpOgvi+2vSjVCNR2yBob4y5pcr9P4jIOhvqadBEhL/cnMjmA3k89MFavnhoIDERQU6XVf8qK2DbPCsA9n1vLQLTe4L1o6uAKdXo1DYIikRkoDFmKYCIDACK7CvLBgdSYM1MCIqy2qqDmlS57f43IOKsk5gdn5zuhr8vZdKsNXwwoR/+fl4y8VnJMev6/xXTrSmgI+Nh6F+sTuDACKerU0qdp9oGwURgpruvACAbuNOekmySsw82zoGiHOA0HeTiA4GRJ8MhqEm10LC2tQ+KYvpgF4/O28mL/w3gtyMv8exmkOy98OMbVpCW5EHrvtYAsE7DwVdHXCvV2J3TVUMiEg5gjMkTkYeNMS/bVdjpXPBVQ5UVUJxrdWgWHoWio+5/s09zO8e6XZp/2kNW+LjwDW5aJUBqOuuocju4mTWffkOeQtkY2P8jLJ8GWz4HBC6+yVoDOO4Sp6tTSp2jC75q6Lhq8wv9Cnj5Aupyho+v+8O4CTRtX/vnlZe4A+JkgJTnH+X979ZSln+EW1uHEmHyrMeP7IK0VVaAVJxm3IGvP4TFQHgshLeC8Kq33f+GtrDqrU8VZbD5U2sAWPpqq8mn/4NW+39EXP3WopSqFxdyXu/BbSE18AuwFkKvshi6H3DNRbcx/NWlzM6oYXI6Y6C04KdnGwWZ1uWWx38y1sDWDCivtjKa+FqvF97K+glrdfL28bAIiwE//wv//YqyYfW/rCagvHRo0h6GvQjdb4cAnX1VKU92IUHgFVNMnE1MRBCvju7JuLdX8NuPNjB1VA/keH+BiPUhGhB69umUjbE+jPPS3QGRXiUs0uHwVtj5bc1NVCHNq4RDzKlBER5rhYV/cM2ve2QXLH8d1r0PZQWQcBkM/5s1DURDbrpSStWZMwaBiByj5g98AbzwusmaDezYjF8Nvoi/zd/OJQlNGNe3zbkfRORkk1XLxNPvV5xXc1DkZVhX8uz73gqU6oKiTobC8YDIWAvbv7IGfHW7FfreBzFJ5167UqpRO2MQGGPC6quQxm6Se3K6P32+maTYCLq3jrTnhQLDrZ/mnU+/T2khHDtw+rOLA+us5qngpjDoEWsRmLAW9tSrlGrwznuuIac4NddQbeQUljL81aUA/PfBgUSF1EHbvV3KS6w+CL38UymvcMEL06jaiQz25/Wxvcg8VsKUD9dRWdmAQ9YvQENAKQVoENS5pLhInr6+K99ty+S1hTudLkcppc5Kg8AGY/rEc1PPWKYu2M6SHZlOl6OUUmekQWADEeGZm7rRsXkokz9YR0ZO45qWSSnlXTQIbHJ8crrS8komvb+G0vJKp0tSSqkaaRDYqH10KM/fmsTafTn8Zd4Wp8tRSqka2RoEInKtiGwTkZ0i8tgZ9rtURCpE5FY763HCsMQY7hrQlne/38Pn6zOcLkcppX7CtiAQEV9gGnAd0BW4XUS6nma/54Cv7arFaY8P68wlbaJ4bG4KOw+ffhZTpZRygp1nBL2BncaYVGNMKfABMLKG/R4E5gKHbazFUS5fH6bd0YtAly/3vbeagpJyp0tSSqkT7AyCWGB/lftp7m0niEgscBMw/UwHEpEJIrJKRFZlZjbOyzFbRgTy6u092ZWZzyNz1lNcVuF0SUopBdgbBDVNU119qO3LwKPGmDN+Khpj3jDGJBtjkqOjo+uqvno3oEMzHr22M/M2HOS6V5awPPWI0yUppZStQZAGVJ17OQ6o3luaDHwgInuAW4F/iMiNNtbkuF9e3p5Z9/ShotIw+o3lPP7RBnKLypwuSynlxewMgpVARxFpKyL+wGjgs6o7GGPaGmMSjDEJwBzgfmPMJzbW1CAM6NCMrx8exC8HtWP2yn1c89Iivtp40OmylFJeyrYgMMaUAw9gXQ20BfjQGLNJRCaKyES7XrexCPL35fFhXfh00kCahgYw8b3VTPx/qzmcV3z2JyulVB3SaagbgLKKSt5cksrLC3YQ6OfDE8O7cFty65MrnSml1AXSaagbOJevD/df0YGvHx5El5hwHp27gTveXMGerAKnS1NKeQENggakbbMQ/n1vX569OZGNGbkMfXkxr3+3i/IKnadIKWUfDYIGxsdHuL13PAt+dTlXdIrmua+2MnLaMjam5zpdmlLKQ2kQNFAtwgP557hkpo/txeFjJYyctoxnv9xCUakORFNK1S0Nggbu2m4xLPjV5dyWHMc/F6Vy7SuL+X5nltNlKaU8iAZBIxAR5OLZm5N4/94+CHDHjBU8OieF3EIdiKaUunAaBI1I//bN+OrhQUy8vD1z1qQxeOoivtxwgMZ2CbBSqmHRIGhkAl2+PHZdZz6dNIAW4QHcN2sNv/x/qzmYqwPRlFLnR4OgkeoWG8En9w/gt8M6s3hHJte8tIhZK/ZSWalnB0qpc6NB0Ij5+fowYVB7vn54EIlxETzx8UZGv7mc1Exd/EYpVXsaBB6gTdMQZt3Th+dvSWLrgTyufWUJ0xbupEwHoimlakGDwEOICLdd2poF/3c513RpwQtfb+P6vy8lJS3H6dKUUg2cBoGHaR4WyLQxvXhj3CVkF5Zy47RlPPPFZgpLdXlMpVTNNAg81JCLWzL/V5czunc8by7ZzdCXF7N0hw5EU0r9lAaBBwsPdPGXmxKZPaEvLh8fxr61gl//Zz05haVOl6aUakA0CLxAn3ZNmTf5Mh64sgOfrE1n8EuL+Hx9hg5EU0oBGgReI9Dly6+HduKzBwbSKjKIB/+9lnFv/cjSHVkaCEp5OV2hzAtVVBre/X4Pr3+3i6z8Ejq3DOOey9pxQ/dW+PvpdwOlPNGZVijTIPBiJeUVfLoug7eW7GbboWM0Dwvgzv4JjOkTT2Swv9PlKaXqkAaBOiNjDIt3ZDFjSSpLdmQR5PLlZ8lx3DWgLQnNQpwuTylVBzQIVK1tPZjHjCW7+XRdOuWVhiFdW3DPZe1IbhOFiDhdnlLqPGkQqHN2OK+YmT/s5b0Ve8kpLKN760juvawt117cEj9f7UdQqrHRIFDnrbC0nLmr03hr6W72HCkkNjKI8QMSGN07ntAAP6fLU0rVkgaBumAVlYZvtxxixpLd/LjnKGEBftzeJ55f9E+gVWSQ0+Uppc5Cg0DVqfX7c3hzSSpfbjyIAMOTYrhnYDsS4yKcLk0pdRoaBMoWadmFvLtsDx+s3E9+STl92jbhnsvacXXn5vj4aMeyUg2JBoGy1bHiMmav3M87y/aQnlNEu2Yh3DWwLbf0iiPI39fp8pRSaBCoelJeUcm8jQeZsSSVlLRcooJdjOvbhnH9EogOC3C6PKW8mgaBqlfGGFbuyebNJaks2HIIl48PN/ZsxT2XteOiFmFOl6eUVzpTEOj1f6rOiQi92zahd9smpGbm886yPfxn9X4+XJXG5RdFc89lbRnYoZkOUFOqgdAzAlUvsgtKmbViL//6YS+Zx05OdHd99xgC/LQfQSm7adOQajBKyiv4bF0GM6pMdPez5DhGJLWic8swPUtQyiaOBYGIXAu8AvgCM4wxf632+BjgUffdfOA+Y8z6Mx1Tg8AzGGNYsiOLt5buZsmOTCoNtIsOYURiDMOTWtGppfYlKFWXHAkCEfEFtgPXAGnASuB2Y8zmKvv0B7YYY7JF5Drg98aYPmc6rgaB58nKL+HrTQf5IuUAy1OPUGmgY/NQhifFMDwxho7awazUBXMqCPphfbAPdd9/HMAY8+xp9o8CNhpjYs90XA0Cz5Z5rISvNh7gvykH+HHPUYyBTi3CGJ4Uw7DEGDo0D3W6RKUaJaeuGooF9le5nwac6dv+3cCXNtajGoHosADG9UtgXL8EDucV8+VG60xh6oLtvDR/O51bhjHCHQrtojUUlKoLdgZBTb1+NZ5+iMiVWEEw8DSPTwAmAMTHx9dVfaqBax4eyJ39E7izfwIHc4v5cuMBvkg5wIvfbOfFb7bTNSb8RPORLqCj1PlzvGlIRJKAj4HrjDHbz3ZcbRpSB3KLmLfhIF+kZLBmXw4A3WLDGZ7YiuGJMcQ3DXa2QKUaIKf6CPywOouvBtKxOovvMMZsqrJPPPA/4OfGmO9rc1wNAlVVek4RX26w+hTW7c8BICkuguGJVvNR6yYaCkqBs5ePDgNexrp89G1jzDMiMhHAGDNdRGYAtwB73U8pP12hx2kQqNPZf7TwRPPR+rRcALq3jmREYgzDkmKI1XUTlBfTAWXK6+w7Usg8dyhsSLdCoWd85IkzBV1MR3kbDQLl1fZkFfDFBisUNh/IA+CSNlEnQqFlRKDDFSplPw0CpdxSM/OZ5+5T2HrwGACXJlihMLRbS2Ii9ExBeSYNAqVqsPOwFQpfpBxg2yErFLrFhjO4Swuu6dqCrjHhOveR8hgaBEqdxc7Dx5i/+TDzNx9k7f4cjIHYyCAGd2nO4K4t6NO2Kf5+Pk6XqdR50yBQ6hxkHith4dbDfLP5EEt3ZlJcVklYgB+Xd4rmmq4tuKJTcyKCXE6XqdQ50SBQ6jwVlVawbGcW8zcf4tuth8jKL8XPx1p453gTko5VUI2BBoFSdaCy0rB2fw4LthxiweZD7DicD0DnlmEnQiExNgIfH+1XUA2PBoFSNtidVcC3Ww4xf/MhVu45SqWB5mEBXN2lBUO6tqBf+6YEunT1NdUwaBAoZbPsglIWbjvMgi2HWLQtk4LSCoL9fRnUMZrBXVtwVefmNAnxd7pM5cU0CJSqR8VlFSxPPcL8zYdYsOUQh/JK8BFIbtOEwV2bc03XlrTV2VJVPdMgUMohxhg2pucxf/NB5m85zBb3yOb20SEM7mo1IfVoHYWv9isom2kQKNVApGUXsmDzIRZsOczy1COUVxqahvhzVefmXNO1BQM7NiPY385lQpS30iBQqgHKKy7ju22ZLNh8iIXbDnOsuBx/Px+6xoTTPS6CxLhIkuIiaB8dqmcM6oJpECjVwJWWV7Jyz1G+23aY9Wm5bEzPpbC0AoAgly/dYsNJjLWCITEugrZNQ/QyVXVONAiUamQqKg27s/JJSct1/+SwKSOPkvJKAMIC/OgWG3EiGJJiI2ndJEjnRlKn5dTi9Uqp8+TrI3RoHkaH5mHc3CsOgPKKSnYczmdDWi4p6TlsSMvlnWV7KK2wwiEiyGUFgzsgkuIiiYkI1HBQZ6VnBEo1YqXllWw/dIyUtFw2pOeQkpbLtoPHKK+0/q6bhfqTGOvub3AHRPNwXX/BG+kZgVIeyt/Ph26xEXSLjQDiAWscw9aDx0hJs4JhQ1oui7bvwJ0NtAgPONHfcPwMomlogHO/hHKcBoFSHibQ5UuP1pH0aB15YlthaTmbM/LcZw5Wn8O3Ww9xvEEgNjLolP6Gi1uFExns0mYlL6FBoJQXCPb3IzmhCckJTU5sO1ZcxqaMvJNnDum5fLnx4InHwwL8iI0KIi4qiLioYPe/QcRGWrc1KDyHBoFSXios0EXfdk3p267piW05haVsTM9j68E80rKL3D+FLE89Sn5J+SnPD/H3PREQ1QMjNjKIJiH+GhSNhAaBUuqEyGB/BnZsxsCOzU7Zbowhr6ic/dmFpOecDIi07CLSs4tYuecoecWnBkWQy/fEWURcVPBPwqKpBkWDoUGglDorESEi2EVE8PGO6Z/KLSoj3R0Q1cNi7f4ccgrLTtk/0OVDbGTVZqeqYRFEdGiABkU90SBQStWJiCAXEUEuurYKr/HxY8VlVkAcrR4WRaSk5ZBdLSgC/HxoER5IVLCLiGB/ooJdRAa5iAz2JzLYRVSwPxHubVHubeGBLh1xfR40CJRS9SIs0EXnli46t6w5KApKyt3hcLLJ6WBeMTmFZeQUlrL3SAE5hWXkFpXV+HwAESuQooL93f9awRFRJSwi3dusYLHCJDzQz6vPPjQIlFINQkiAHxe1COOiFmFn3K+i0pBXVEZ2YSk5RVZIWGHhvl1URrb7dlZ+KTsz88kpKONYtc7uqnx9hIggd0gEVT3bsAIjLNCPkADrJ9jf98S/oQF+BPv7ERLgS5DLt9GGiQaBUqpR8fURokL8iTrHFd/KKirdAVJGblEp2QVlpwZJUan1WGEZB/OK2XrwGDmFpRS4J/87GxEI8T81KELcIREc4EeIv++J0AgJ8Dtl35Bqjwf7+xEa4Eegy6dewkWDQCnlFVy+PjQNDTjnUdSl5ZXkl5RTUFJOYWkFBaXW7YKSCgpLyykorbAeK7FuF5aWk19S4b5fzpGCUvYdLaSg5ORzK2s5s0/1cBnTJ557Lmt3Hr/9mWkQKKXUGfj7+dDEz7/O1pw2xlBSXlktWNxhUnoyYPKPB02VwGlm01QgGgRKKVWPRIRAly+BLl+ann33euHjdAFKKaWcpUGglFJeToNAKaW8nK1BICLXisg2EdkpIo/V8LiIyKvux1NEpJed9SillPop24JARHyBacB1QFfgdhHpWm2364CO7p8JwOt21aOUUqpmdp4R9AZ2GmNSjTGlwAfAyGr7jARmGstyIFJEYmysSSmlVDV2BkEssL/K/TT3tnPdBxGZICKrRGRVZmZmnReqlFLezM4gqGlcdPXxdLXZB2PMG8aYZGNMcnR0dJ0Up5RSymLngLI0oHWV+3FAxnnsc4rVq1dnicje86ypGZB1ns/1RPp+nErfj5P0vTiVJ7wfbU73gJ1BsBLoKCJtgXRgNHBHtX0+Ax4QkQ+APkCuMebAmQ5qjDnvUwIRWWWMST7f53safT9Ope/HSfpenMrT3w/bgsAYUy4iDwBfA77A28aYTSIy0f34dGAeMAzYCRQC4+2qRymlVM1snWvIGDMP68O+6rbpVW4bYJKdNSillDozbxtZ/IbTBTQw+n6cSt+Pk/S9OJVHvx9ifSlXSinlrbztjEAppVQ1GgRKKeXlvCYIzjYBnjcRkdYislBEtojIJhGZ7HRNThMRXxFZKyL/dboWp4lIpIjMEZGt7v8j/ZyuySkiMsX9N7JRRP4tIoFO12QHrwiCWk6A503Kgf8zxnQB+gKTvPz9AJgMbHG6iAbiFeArY0xnoDte+r6ISCzwEJBsjOmGdRn8aGersodXBAG1mwDPaxhjDhhj1rhvH8P6Q//JHE/eQkTigOHADKdrcZqIhAODgLcAjDGlxpgcR4tylh8QJCJ+QDBnmfmgsfKWIKjV5HbeSEQSgJ7ACodLcdLLwG+ASofraAjaAZnAO+6mshkiEuJ0UU4wxqQDLwL7gANYMx9842xV9vCWIKjV5HbeRkRCgbnAw8aYPKfrcYKIjAAOG2NWO11LA+EH9AJeN8b0BAoAr+xTE5EorJaDtkArIERExjpblT28JQjOeXI7TyciLqwQmGWM+cjpehw0ALhBRPZgNRleJSLvOVuSo9KANGPM8TPEOVjB4I0GA7uNMZnGmDLgI6C/wzXZwluC4MQEeCLij9Xh85nDNTlGRASrDXiLMeYlp+txkjHmcWNMnDEmAev/xf+MMR75ra82jDEHgf0i0sm96Wpgs4MlOWkf0FdEgt1/M1fjoR3nts411FCcbgI8h8ty0gBgHLBBRNa5t/3WPTeUUg8Cs9xfmlLx0skgjTErRGQOsAbrSru1eOhUEzrFhFJKeTlvaRpSSil1GhoESinl5TQIlFLKy2kQKKWUl9MgUEopL6dBoJSbiFSIyLoqP3U2olZEEkRkY10dT6m65BXjCJSqpSJjTA+ni1CqvukZgVJnISJ7ROQ5EfnR/dPBvb2NiHwrIinuf+Pd21uIyMcist79c3xaAl8RedM9v/03IhLk3v8hEdnsPs4HDv2ayotpECh1UlC1pqFRVR7LM8b0Bl7Dmq0U9+2ZxpgkYBbwqnv7q8AiY0x3rHl6jo9i7whMM8ZcDOQAt7i3Pwb0dB9noj2/mlKnpyOLlXITkXxjTGgN2/cAVxljUt2T9R00xjQVkSwgxhhT5t5+wBjTTEQygThjTEmVYyQA840xHd33HwVcxpg/i8hXQD7wCfCJMSbf5l9VqVPoGYFStWNOc/t0+9SkpMrtCk720Q3HWkHvEmC1exEUpeqNBoFStTOqyr8/uG9/z8mlC8cAS923vwXugxNrIYef7qAi4gO0NsYsxFocJxL4yVmJUnbSbx5KnRRUZTZWsNbtPX4JaYCIrMD68nS7e9tDwNsi8gjWql7HZ+mcDLwhIndjffO/D2uFq5r4Au+JSATWAkpTvXxpSOUA7SNQ6izcfQTJxpgsp2tRyg7aNKSUUl5OzwiUUsrL6RmBUkp5OQ0CpZTychoESinl5TQIlFLKy2kQKKWUl/v/ckLUDU9pVAQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history_3.history['accuracy'], label = \"acc_m_3\")\n",
    "plt.plot(history_3.history['val_accuracy'], label = \"val_acc_m_3\")\n",
    "\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "plt.plot(history_3.history['loss'], label = \"loss_3\")\n",
    "plt.plot(history_3.history['val_loss'], label = \"val_loss_3\")\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3eb12850",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history_3.history['accuracy'], label = \"acc_m_3\")\n",
    "plt.plot(history_3.history['val_accuracy'], label = \"val_acc_m_3\")\n",
    "\n",
    "plt.plot(history_2.history['accuracy'], label = \"acc_mod_2\")\n",
    "plt.plot(history_2.history['accuracy'], label = \"val_acc_mod_2\")\n",
    "\n",
    "\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "9824ae79",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_3\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " embedding_3 (Embedding)     (None, 100, 64)           640000    \n",
      "                                                                 \n",
      " conv1d_1 (Conv1D)           (None, 98, 128)           24704     \n",
      "                                                                 \n",
      " global_average_pooling1d_3   (None, 128)              0         \n",
      " (GlobalAveragePooling1D)                                        \n",
      "                                                                 \n",
      " dense_10 (Dense)            (None, 24)                3096      \n",
      "                                                                 \n",
      " dropout_2 (Dropout)         (None, 24)                0         \n",
      "                                                                 \n",
      " dense_11 (Dense)            (None, 1)                 25        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 667,825\n",
      "Trainable params: 667,825\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "INFO:tensorflow:Assets written to: /models/model_4\\assets\n",
      "Epoch 1/100\n",
      "625/625 - 7s - loss: 0.5708 - accuracy: 0.6901 - val_loss: 0.4592 - val_accuracy: 0.7843 - 7s/epoch - 11ms/step\n",
      "Epoch 2/100\n",
      "625/625 - 7s - loss: 0.3789 - accuracy: 0.8390 - val_loss: 0.4468 - val_accuracy: 0.7928 - 7s/epoch - 11ms/step\n",
      "Epoch 3/100\n",
      "625/625 - 7s - loss: 0.2916 - accuracy: 0.8855 - val_loss: 0.4590 - val_accuracy: 0.7965 - 7s/epoch - 11ms/step\n",
      "Epoch 4/100\n",
      "625/625 - 7s - loss: 0.2299 - accuracy: 0.9152 - val_loss: 0.5129 - val_accuracy: 0.7878 - 7s/epoch - 11ms/step\n",
      "Epoch 5/100\n",
      "625/625 - 7s - loss: 0.1809 - accuracy: 0.9372 - val_loss: 0.5988 - val_accuracy: 0.7872 - 7s/epoch - 10ms/step\n",
      "Epoch 6/100\n",
      "625/625 - 8s - loss: 0.1376 - accuracy: 0.9538 - val_loss: 0.6698 - val_accuracy: 0.7827 - 8s/epoch - 12ms/step\n",
      "Epoch 7/100\n",
      "625/625 - 7s - loss: 0.0997 - accuracy: 0.9688 - val_loss: 0.8003 - val_accuracy: 0.7816 - 7s/epoch - 11ms/step\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, GlobalAveragePooling1D, Convolution1D, Dropout\n",
    "from keras.callbacks import LearningRateScheduler, ModelCheckpoint, EarlyStopping\n",
    "\n",
    "  \n",
    "max_epochs = 100 # number epochs to use\n",
    "\n",
    "embedding_dim = 64\n",
    "\n",
    "model_4 = Sequential()  # define the model\n",
    "model_4.add(Embedding(max_words, embedding_dim, input_length = max_length))\n",
    "model_4.add(Convolution1D(128,3, activation='relu'))  # add the convolutional Layer\n",
    "model_4.add(GlobalAveragePooling1D())                 # add the average polling\n",
    "model_4.add(Dense(24, activation='relu'))             # add the dense layers\n",
    "model_4.add(Dropout(0.25))                            # add the drop out layer\n",
    "model_4.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "model_4.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy']) # compile the model\n",
    "\n",
    "model_4.summary() # model summarize\n",
    "\n",
    "save_model4 = model_4.save(\"/models/model_4\") # saving model\n",
    "\n",
    "early_stopping = EarlyStopping(monitor=\"val_loss\", \n",
    "                               patience=5, \n",
    "                               restore_best_weights=True)\n",
    "\n",
    "# fit the model\n",
    "history_4 = model_4.fit(x_train, y_train, \n",
    "                        epochs=max_epochs,\n",
    "                        batch_size=32, \n",
    "                        validation_data=(x_test, y_test),\n",
    "                        callbacks=[early_stopping],\n",
    "                        verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8aa65163",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train accuracy for Conv2: 90.61\n",
      "Test accuracy for  Conv2: 79.28\n"
     ]
    }
   ],
   "source": [
    "# model evaluation\n",
    "train_conv2_results =model_4.evaluate(x_train, y_train, verbose=0, batch_size=32)\n",
    "test_conv2_results = model_4.evaluate(x_test, y_test, verbose=0, batch_size=32)\n",
    "print(f'Train accuracy for Conv2: {train_conv2_results[1]*100:0.2f}')\n",
    "print(f'Test accuracy for  Conv2: {test_conv2_results[1]*100:0.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f220013b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.11796293]\n",
      " [0.98103034]]\n"
     ]
    }
   ],
   "source": [
    "#check the model\n",
    "sentences = ['lesbian consider father indiana amazing one', \"terrifying server whole heartedly care guest dining experience\"]\n",
    "sequencess = token.texts_to_sequences(sentences)\n",
    "\n",
    "padded = pad_sequences(sequencess, maxlen=max_length, padding=padding_type, truncating=trunc_type)\n",
    "\n",
    "# make a prediction\n",
    "pred_4 = model_4.predict(padded)\n",
    "print(pred_4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e7769f8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history_4.history['accuracy'], label = \"acc_m_4\")\n",
    "plt.plot(history_4.history['val_accuracy'], label = \"val_acc_m_4\")\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "plt.plot(history_4.history['loss'], label = \"loss_4\")\n",
    "plt.plot(history_4.history['val_loss'], label = \"val_loss_4\")\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fbca2f2",
   "metadata": {},
   "source": [
    "**SimpleRNN**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f6f58397",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_4\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " embedding_4 (Embedding)     (None, 100, 64)           640000    \n",
      "                                                                 \n",
      " simple_rnn (SimpleRNN)      (None, 100, 64)           8256      \n",
      "                                                                 \n",
      " simple_rnn_1 (SimpleRNN)    (None, 32)                3104      \n",
      "                                                                 \n",
      " dense_12 (Dense)            (None, 16)                528       \n",
      "                                                                 \n",
      " dropout_3 (Dropout)         (None, 16)                0         \n",
      "                                                                 \n",
      " dense_13 (Dense)            (None, 1)                 17        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 651,905\n",
      "Trainable params: 651,905\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "INFO:tensorflow:Assets written to: /models/model_5\\assets\n",
      "Epoch 1/30\n",
      "625/625 - 19s - loss: 0.6928 - accuracy: 0.5212 - val_loss: 0.6921 - val_accuracy: 0.5249 - 19s/epoch - 31ms/step\n",
      "Epoch 2/30\n",
      "625/625 - 19s - loss: 0.6922 - accuracy: 0.5231 - val_loss: 0.6919 - val_accuracy: 0.5249 - 19s/epoch - 31ms/step\n",
      "Epoch 3/30\n",
      "625/625 - 19s - loss: 0.6930 - accuracy: 0.5228 - val_loss: 0.6918 - val_accuracy: 0.5249 - 19s/epoch - 31ms/step\n",
      "Epoch 4/30\n",
      "625/625 - 21s - loss: 0.6921 - accuracy: 0.5226 - val_loss: 0.6915 - val_accuracy: 0.5249 - 21s/epoch - 34ms/step\n",
      "Epoch 5/30\n",
      "625/625 - 19s - loss: 0.6919 - accuracy: 0.5231 - val_loss: 0.6920 - val_accuracy: 0.5249 - 19s/epoch - 30ms/step\n",
      "Epoch 6/30\n",
      "625/625 - 23s - loss: 0.6921 - accuracy: 0.5231 - val_loss: 0.6919 - val_accuracy: 0.5249 - 23s/epoch - 37ms/step\n",
      "Epoch 7/30\n",
      "625/625 - 25s - loss: 0.6921 - accuracy: 0.5231 - val_loss: 0.6919 - val_accuracy: 0.5249 - 25s/epoch - 40ms/step\n",
      "Epoch 8/30\n",
      "625/625 - 23s - loss: 0.6921 - accuracy: 0.5231 - val_loss: 0.6919 - val_accuracy: 0.5249 - 23s/epoch - 37ms/step\n",
      "Epoch 9/30\n",
      "625/625 - 23s - loss: 0.6921 - accuracy: 0.5231 - val_loss: 0.6919 - val_accuracy: 0.5249 - 23s/epoch - 37ms/step\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, GlobalAveragePooling1D, Convolution1D, Dropout, SimpleRNN\n",
    "from keras.callbacks import LearningRateScheduler, ModelCheckpoint, EarlyStopping\n",
    "\n",
    "\n",
    "max_epochs = 30 # number epochs to use\n",
    "\n",
    "model_5 = Sequential()  # define the model\n",
    "model_5.add(Embedding(max_words, embedding_dim, input_length = max_length))\n",
    "model_5.add(SimpleRNN(64, return_sequences=True)) # add the RNN Layer\n",
    "model_5.add(SimpleRNN(32))\n",
    "model_5.add(Dense(16, activation='relu'))             # add the dense layers   \n",
    "model_5.add(Dropout(0.25)) \n",
    "model_5.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "# compile the mode\n",
    "model_5.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])\n",
    "\n",
    "model_5.summary() # model summarize\n",
    "\n",
    "save_model_5 = model_5.save(\"/models/model_5\") # saving model\n",
    "\n",
    "early_stopping = EarlyStopping(monitor=\"val_loss\", \n",
    "                               patience=5, \n",
    "                               restore_best_weights=True)\n",
    "# fit the model\n",
    "history_5 = model_5.fit(x_train, y_train, \n",
    "                        epochs=max_epochs,\n",
    "                        batch_size=32, \n",
    "                        validation_data=(x_test, y_test),\n",
    "                        callbacks=[early_stopping],\n",
    "                        verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "fd4f642b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train accuracy for SRNN: 52.31\n",
      "Test accuracy for  SRNN: 52.49\n"
     ]
    }
   ],
   "source": [
    "# model evaluation\n",
    "train_SRNN_results =model_5.evaluate(x_train, y_train, verbose=0, batch_size=32)\n",
    "test_SRNN_results = model_5.evaluate(x_test, y_test, verbose=0, batch_size=32)\n",
    "print(f'Train accuracy for SRNN: {train_SRNN_results[1]*100:0.2f}')\n",
    "print(f'Test accuracy for  SRNN: {test_SRNN_results[1]*100:0.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "8b866211",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:5 out of the last 5 calls to <function Model.make_predict_function.<locals>.predict_function at 0x000001D7A3BF0430> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "[[0.47722352]\n",
      " [0.47296005]]\n"
     ]
    }
   ],
   "source": [
    "#check the model\n",
    "sentences = ['lesbian consider father indiana amazing one', \"terrifying server whole heartedly care guest dining experience\"]\n",
    "sequencess = token.texts_to_sequences(sentences)\n",
    "\n",
    "padded = pad_sequences(sequencess, maxlen=max_length, padding=padding_type, truncating=trunc_type)\n",
    "\n",
    "# make a prediction\n",
    "pred_5 = model_5.predict(padded)\n",
    "print(pred_5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d67ef21f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history_5.history['accuracy'], label = \"acc_m_5\")\n",
    "plt.plot(history_5.history['val_accuracy'], label = \"val_acc_m_5\")\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "plt.plot(history_5.history['loss'], label = \"loss_5\")\n",
    "plt.plot(history_5.history['val_loss'], label = \"val_loss_5\")\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58b1b572",
   "metadata": {},
   "source": [
    "**SimpleRNN & Bidirectional**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "b8071ab3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_5\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " embedding_5 (Embedding)     (None, 100, 64)           640000    \n",
      "                                                                 \n",
      " bidirectional (Bidirectiona  (None, 100, 128)         16512     \n",
      " l)                                                              \n",
      "                                                                 \n",
      " simple_rnn_3 (SimpleRNN)    (None, 32)                5152      \n",
      "                                                                 \n",
      " dense_14 (Dense)            (None, 32)                1056      \n",
      "                                                                 \n",
      " dense_15 (Dense)            (None, 1)                 33        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 662,753\n",
      "Trainable params: 662,753\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "INFO:tensorflow:Assets written to: /models/model_6\\assets\n",
      "Epoch 1/50\n",
      "625/625 - 33s - loss: 0.6919 - accuracy: 0.5271 - val_loss: 0.6735 - val_accuracy: 0.5800 - 33s/epoch - 53ms/step\n",
      "Epoch 2/50\n",
      "625/625 - 31s - loss: 0.6343 - accuracy: 0.6619 - val_loss: 0.6485 - val_accuracy: 0.6428 - 31s/epoch - 50ms/step\n",
      "Epoch 3/50\n",
      "625/625 - 31s - loss: 0.5358 - accuracy: 0.7538 - val_loss: 0.5870 - val_accuracy: 0.7032 - 31s/epoch - 50ms/step\n",
      "Epoch 4/50\n",
      "625/625 - 31s - loss: 0.4528 - accuracy: 0.8060 - val_loss: 0.6102 - val_accuracy: 0.7039 - 31s/epoch - 50ms/step\n",
      "Epoch 5/50\n",
      "625/625 - 31s - loss: 0.4689 - accuracy: 0.7959 - val_loss: 0.6249 - val_accuracy: 0.7007 - 31s/epoch - 50ms/step\n",
      "Epoch 6/50\n",
      "625/625 - 31s - loss: 0.4603 - accuracy: 0.8001 - val_loss: 0.6261 - val_accuracy: 0.7004 - 31s/epoch - 50ms/step\n",
      "Epoch 7/50\n",
      "625/625 - 32s - loss: 0.4602 - accuracy: 0.8005 - val_loss: 0.6222 - val_accuracy: 0.7007 - 32s/epoch - 51ms/step\n",
      "Epoch 8/50\n",
      "625/625 - 32s - loss: 0.4601 - accuracy: 0.8007 - val_loss: 0.6136 - val_accuracy: 0.7003 - 32s/epoch - 51ms/step\n",
      "Epoch 9/50\n",
      "625/625 - 31s - loss: 0.4594 - accuracy: 0.8007 - val_loss: 0.6098 - val_accuracy: 0.7015 - 31s/epoch - 50ms/step\n",
      "Epoch 10/50\n",
      "625/625 - 31s - loss: 0.4599 - accuracy: 0.8008 - val_loss: 0.6093 - val_accuracy: 0.7032 - 31s/epoch - 50ms/step\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, GlobalAveragePooling1D, Convolution1D, Dropout, SimpleRNN, Bidirectional\n",
    "from keras.callbacks import LearningRateScheduler, ModelCheckpoint, EarlyStopping\n",
    "\n",
    "max_epochs = 50 # number epochs to use\n",
    "\n",
    "model_6 = Sequential()  # define the model\n",
    "model_6.add(Embedding(max_words, embedding_dim, input_length = max_length))\n",
    "model_6.add(Bidirectional(SimpleRNN(64, return_sequences=True))) # add the RNN Layer\n",
    "model_6.add(SimpleRNN(32))  \n",
    "model_6.add(Dense(32, activation='relu'))             # add the dense layers                   \n",
    "model_6.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "# compile the model\n",
    "model_6.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])\n",
    "model_6.summary() # model summarize\n",
    "\n",
    "save_model6 = model_6.save(\"/models/model_6\") # saving model\n",
    "\n",
    "early_stopping = EarlyStopping(monitor=\"val_loss\", \n",
    "                               patience=7, \n",
    "                               restore_best_weights=True)\n",
    "# fit the model\n",
    "history_6 = model_6.fit(x_train, y_train, \n",
    "                        epochs=max_epochs,\n",
    "                        batch_size=32, \n",
    "                        validation_data=(x_test, y_test),\n",
    "                        callbacks=[early_stopping],\n",
    "                        verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "5a84ffb4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train accuracy for SRNN_Bidirectional: 80.92\n",
      "Test accuracy for  SRNN_Bidirectional: 70.32\n"
     ]
    }
   ],
   "source": [
    "# model evaluation\n",
    "train_SRNN_Bid_results =model_6.evaluate(x_train, y_train, verbose=0, batch_size=32)\n",
    "test_SRNN_Bid_results = model_6.evaluate(x_test, y_test, verbose=0, batch_size=32)\n",
    "print(f'Train accuracy for SRNN_Bidirectional: {train_SRNN_Bid_results[1]*100:0.2f}')\n",
    "print(f'Test accuracy for  SRNN_Bidirectional: {test_SRNN_Bid_results[1]*100:0.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "f8c76d5c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:6 out of the last 6 calls to <function Model.make_predict_function.<locals>.predict_function at 0x000001D7A3F34F70> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "[[0.22997513]\n",
      " [0.29760385]]\n"
     ]
    }
   ],
   "source": [
    "#check the model\n",
    "sentences = ['lesbian consider father indiana amazing one', \"terrifying server whole heartedly care guest dining experience\"]\n",
    "sequencess = token.texts_to_sequences(sentences)\n",
    "\n",
    "padded = pad_sequences(sequencess, maxlen=max_length, padding=padding_type, truncating=trunc_type)\n",
    "\n",
    "# make a prediction\n",
    "pred_6 = model_6.predict(padded)\n",
    "print(pred_6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "bb60d5ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history_6.history['accuracy'], label = \"acc_m_6\")\n",
    "plt.plot(history_6.history['val_accuracy'], label = \"val_acc_m_6\")\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "plt.plot(history_6.history['loss'], label = \"loss_6\")\n",
    "plt.plot(history_6.history['val_loss'], label = \"val_loss_6\")\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76d1ddf4",
   "metadata": {},
   "source": [
    "**LSTM**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "284a1d74",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_22\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " embedding_22 (Embedding)    (None, 100, 64)           640000    \n",
      "                                                                 \n",
      " bidirectional_14 (Bidirecti  (None, 64)               24832     \n",
      " onal)                                                           \n",
      "                                                                 \n",
      " dense_46 (Dense)            (None, 24)                1560      \n",
      "                                                                 \n",
      " dropout_15 (Dropout)        (None, 24)                0         \n",
      "                                                                 \n",
      " dense_47 (Dense)            (None, 1)                 25        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 666,417\n",
      "Trainable params: 666,417\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as lstm_cell_19_layer_call_fn, lstm_cell_19_layer_call_and_return_conditional_losses, lstm_cell_20_layer_call_fn, lstm_cell_20_layer_call_and_return_conditional_losses, lstm_cell_19_layer_call_fn while saving (showing 5 of 10). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: /models/model_7\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: /models/model_7\\assets\n",
      "WARNING:absl:<keras.layers.recurrent.LSTMCell object at 0x000001D822014340> has the same name 'LSTMCell' as a built-in Keras object. Consider renaming <class 'keras.layers.recurrent.LSTMCell'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
      "WARNING:absl:<keras.layers.recurrent.LSTMCell object at 0x000001D8220138E0> has the same name 'LSTMCell' as a built-in Keras object. Consider renaming <class 'keras.layers.recurrent.LSTMCell'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "625/625 - 159s - loss: 0.5332 - accuracy: 0.7294 - val_loss: 0.4323 - val_accuracy: 0.7970 - lr: 0.0010 - 159s/epoch - 254ms/step\n",
      "Epoch 2/50\n",
      "625/625 - 145s - loss: 0.3361 - accuracy: 0.8637 - val_loss: 0.4283 - val_accuracy: 0.8051 - lr: 0.0010 - 145s/epoch - 233ms/step\n",
      "Epoch 3/50\n",
      "625/625 - 146s - loss: 0.2361 - accuracy: 0.9099 - val_loss: 0.4921 - val_accuracy: 0.8004 - lr: 0.0010 - 146s/epoch - 234ms/step\n",
      "Epoch 4/50\n",
      "625/625 - 148s - loss: 0.1685 - accuracy: 0.9374 - val_loss: 0.5736 - val_accuracy: 0.7910 - lr: 0.0010 - 148s/epoch - 237ms/step\n",
      "Epoch 5/50\n",
      "625/625 - 147s - loss: 0.1212 - accuracy: 0.9535 - val_loss: 0.7385 - val_accuracy: 0.7919 - lr: 0.0010 - 147s/epoch - 235ms/step\n",
      "Epoch 6/50\n",
      "625/625 - 153s - loss: 0.0864 - accuracy: 0.9650 - val_loss: 1.0036 - val_accuracy: 0.7888 - lr: 0.0010 - 153s/epoch - 245ms/step\n",
      "Epoch 7/50\n",
      "625/625 - 150s - loss: 0.0684 - accuracy: 0.9726 - val_loss: 1.1700 - val_accuracy: 0.7793 - lr: 0.0010 - 150s/epoch - 241ms/step\n",
      "Epoch 8/50\n",
      "625/625 - 156s - loss: 0.0541 - accuracy: 0.9768 - val_loss: 1.3492 - val_accuracy: 0.7811 - lr: 0.0010 - 156s/epoch - 250ms/step\n",
      "Epoch 9/50\n",
      "625/625 - 153s - loss: 0.0454 - accuracy: 0.9815 - val_loss: 1.5442 - val_accuracy: 0.7783 - lr: 0.0010 - 153s/epoch - 245ms/step\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, GlobalAveragePooling1D, Convolution1D, Dropout, LSTM, Bidirectional\n",
    "from keras.callbacks import LearningRateScheduler, ModelCheckpoint\n",
    "\n",
    "# define function for lerning rate schedule \n",
    "def scheduler(epoch, lr): \n",
    "      if epoch < 10:\n",
    "        return lr\n",
    "      else:\n",
    "        return lr * tf.math.exp(-0.1)\n",
    "\n",
    "max_epochs = 50 # number epochs to use\n",
    "\n",
    "model_7 = Sequential()  # define the model\n",
    "model_7.add(Embedding(max_words, embedding_dim, input_length = max_length))\n",
    "model_7.add(Bidirectional(LSTM(32)))  # add the Bidirectional & Long Short-Term Memory Layer\n",
    "model_7.add(Dense(24, activation='relu'))             # add the dense layers\n",
    "model_7.add(Dropout(0.5))                            # add the drop out layer\n",
    "model_7.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "model_7.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy']) # compile the model\n",
    "\n",
    "model_7.summary() # model summarize\n",
    "\n",
    "save_model7 = model_7.save(\"/models/model_7\") # saving model\n",
    "lrate = LearningRateScheduler(scheduler)\n",
    "early_stopping = EarlyStopping(monitor=\"val_loss\", \n",
    "                               patience=7, \n",
    "                               restore_best_weights=True)\n",
    "\n",
    "# fit the model\n",
    "history_7 = model_7.fit(x_train, y_train, \n",
    "                          epochs=max_epochs,\n",
    "                          batch_size=32,\n",
    "                          validation_data=(x_test, y_test),\n",
    "                          callbacks=[early_stopping, l_rate],\n",
    "                          verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "ba2363a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train accuracy for LSTM: 92.80\n",
      "Test accuracy for LSTM: 80.51\n"
     ]
    }
   ],
   "source": [
    "# model evaluation\n",
    "train_LSTM_results =model_7.evaluate(x_train, y_train, verbose=0, batch_size=32)\n",
    "test_LSTM_results = model_7.evaluate(x_test, y_test, verbose=0, batch_size=32)\n",
    "print(f'Train accuracy for LSTM: {train_LSTM_results[1]*100:0.2f}')\n",
    "print(f'Test accuracy for LSTM: {test_LSTM_results[1]*100:0.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "b88da977",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.07097754]\n",
      " [0.9898604 ]]\n"
     ]
    }
   ],
   "source": [
    "#check the model\n",
    "sentences = ['lesbian consider father indiana amazing one', \"terrifying server whole heartedly care guest dining experience\"]\n",
    "sequencess = token.texts_to_sequences(sentences)\n",
    "\n",
    "padded = pad_sequences(sequencess, maxlen=max_length, padding=padding_type, truncating=trunc_type)\n",
    "\n",
    "# make a prediction\n",
    "pred_7 = model_7.predict(padded)\n",
    "print(pred_7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "f21a8b9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history_7.history['accuracy'], label = \"acc_m_7\")\n",
    "plt.plot(history_7.history['val_accuracy'], label = \"val_acc_m_7\")\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "plt.plot(history_7.history['loss'], label = \"loss_7\")\n",
    "plt.plot(history_7.history['val_loss'], label = \"val_loss_7\")\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06ad6ec8",
   "metadata": {},
   "source": [
    "**GRU**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "8f0e8fcc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_8\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " embedding_8 (Embedding)     (None, 100, 64)           640000    \n",
      "                                                                 \n",
      " gru_1 (GRU)                 (None, 100, 50)           17400     \n",
      "                                                                 \n",
      " flatten_1 (Flatten)         (None, 5000)              0         \n",
      "                                                                 \n",
      " dense_20 (Dense)            (None, 25)                125025    \n",
      "                                                                 \n",
      " dropout_5 (Dropout)         (None, 25)                0         \n",
      "                                                                 \n",
      " dense_21 (Dense)            (None, 1)                 26        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 782,451\n",
      "Trainable params: 782,451\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as gru_cell_1_layer_call_fn, gru_cell_1_layer_call_and_return_conditional_losses, gru_cell_1_layer_call_fn, gru_cell_1_layer_call_and_return_conditional_losses, gru_cell_1_layer_call_and_return_conditional_losses while saving (showing 5 of 5). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: /models/model_8\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: /models/model_8\\assets\n",
      "WARNING:absl:<keras.layers.recurrent.GRUCell object at 0x000001D7C358B220> has the same name 'GRUCell' as a built-in Keras object. Consider renaming <class 'keras.layers.recurrent.GRUCell'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "625/625 - 15s - loss: 0.5060 - accuracy: 0.7388 - val_loss: 0.4182 - val_accuracy: 0.8064 - 15s/epoch - 24ms/step\n",
      "Epoch 2/50\n",
      "625/625 - 16s - loss: 0.2938 - accuracy: 0.8759 - val_loss: 0.4411 - val_accuracy: 0.8050 - 16s/epoch - 25ms/step\n",
      "Epoch 3/50\n",
      "625/625 - 18s - loss: 0.1907 - accuracy: 0.9268 - val_loss: 0.5179 - val_accuracy: 0.7963 - 18s/epoch - 28ms/step\n",
      "Epoch 4/50\n",
      "625/625 - 16s - loss: 0.1207 - accuracy: 0.9566 - val_loss: 0.6954 - val_accuracy: 0.7899 - 16s/epoch - 26ms/step\n",
      "Epoch 5/50\n",
      "625/625 - 16s - loss: 0.0735 - accuracy: 0.9733 - val_loss: 0.9384 - val_accuracy: 0.7833 - 16s/epoch - 26ms/step\n",
      "Epoch 6/50\n",
      "625/625 - 16s - loss: 0.0427 - accuracy: 0.9851 - val_loss: 1.0403 - val_accuracy: 0.7755 - 16s/epoch - 26ms/step\n",
      "Epoch 7/50\n",
      "625/625 - 17s - loss: 0.0300 - accuracy: 0.9901 - val_loss: 1.2465 - val_accuracy: 0.7729 - 17s/epoch - 27ms/step\n",
      "Epoch 8/50\n",
      "625/625 - 16s - loss: 0.0200 - accuracy: 0.9930 - val_loss: 1.3293 - val_accuracy: 0.7743 - 16s/epoch - 26ms/step\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, GlobalAveragePooling1D, GRU, Flatten, Embedding\n",
    "\n",
    "max_epochs = 50 #number epochs to use\n",
    "\n",
    "model_8 = Sequential()\n",
    "model_8.add(Embedding(max_words, embedding_dim, input_length = max_length))\n",
    "model_8.add(GRU(50, activation='tanh', return_sequences=True)) # add the GRU layer\n",
    "model_8.add(Flatten())                                          # add the Flatten layer\n",
    "model_8.add(Dense(25, activation='relu'))                       # add the Dense layer \n",
    "model_8.add(Dropout(0.25))                                      # add the Dropout layer\n",
    "model_8.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "model_8.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])\n",
    "\n",
    "model_8.summary()\n",
    "\n",
    "early_stopping = EarlyStopping(monitor=\"val_loss\", \n",
    "                               patience=7, \n",
    "                               restore_best_weights=True)\n",
    "\n",
    "save_model_8 = model_8.save(\"/models/model_8\") # saving model\n",
    "lrate = LearningRateScheduler(scheduler)\n",
    "\n",
    "history_8 = model_8.fit(x_train, y_train, \n",
    "                    epochs=max_epochs,\n",
    "                          batch_size=32,\n",
    "                          validation_data=(x_test, y_test),\n",
    "                          callbacks=[early_stopping],\n",
    "                          verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "94db8d28",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train accuracy for GRU: 90.50\n",
      "Test accuracy for GRU : 80.64\n"
     ]
    }
   ],
   "source": [
    "# model evaluation\n",
    "train_gru_results =model_8.evaluate(x_train, y_train, verbose=0, batch_size=32)\n",
    "test_gru_results = model_8.evaluate(x_test, y_test, verbose=0, batch_size=32)\n",
    "print(f'Train accuracy for GRU: {train_gru_results[1]*100:0.2f}')\n",
    "print(f'Test accuracy for GRU : {test_gru_results[1]*100:0.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "0fc61eb0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.17826337]\n",
      " [0.8780551 ]]\n"
     ]
    }
   ],
   "source": [
    "#check the model\n",
    "sentences = ['lesbian consider father indiana amazing one', \"terrifying server whole heartedly care guest dining experience\"]\n",
    "sequencess = token.texts_to_sequences(sentences)\n",
    "\n",
    "padded = pad_sequences(sequencess, maxlen=max_length, padding=padding_type, truncating=trunc_type)\n",
    "\n",
    "# make a prediction\n",
    "pred_8 = model_8.predict(padded)\n",
    "print(pred_8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "0397a0a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history_8.history['accuracy'], label = \"acc_mod_8\")\n",
    "plt.plot(history_8.history['val_accuracy'], label = \"val_acc_mod_8\")\n",
    "\n",
    "#plt.plot(Adam_history.history['binary_accuracy'], label = \"acc_mod_2\")\n",
    "#plt.plot(Adam_history.history['val_binary_accuracy'], label = \"val_acc_mod2\")\n",
    "#\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "507d9d63",
   "metadata": {},
   "source": [
    "GRU & LearningRateScheduler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "cdd98b59",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_23\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " embedding_23 (Embedding)    (None, 100, 64)           640000    \n",
      "                                                                 \n",
      " gru_10 (GRU)                (None, 100, 150)          97200     \n",
      "                                                                 \n",
      " flatten_10 (Flatten)        (None, 15000)             0         \n",
      "                                                                 \n",
      " dense_48 (Dense)            (None, 100)               1500100   \n",
      "                                                                 \n",
      " dropout_16 (Dropout)        (None, 100)               0         \n",
      "                                                                 \n",
      " dense_49 (Dense)            (None, 75)                7575      \n",
      "                                                                 \n",
      " dropout_17 (Dropout)        (None, 75)                0         \n",
      "                                                                 \n",
      " dense_50 (Dense)            (None, 50)                3800      \n",
      "                                                                 \n",
      " dense_51 (Dense)            (None, 1)                 51        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 2,248,726\n",
      "Trainable params: 2,248,726\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as gru_cell_24_layer_call_fn, gru_cell_24_layer_call_and_return_conditional_losses, gru_cell_24_layer_call_fn, gru_cell_24_layer_call_and_return_conditional_losses, gru_cell_24_layer_call_and_return_conditional_losses while saving (showing 5 of 5). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: /models/model_9\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: /models/model_9\\assets\n",
      "WARNING:absl:<keras.layers.recurrent.GRUCell object at 0x000001D820DCB1F0> has the same name 'GRUCell' as a built-in Keras object. Consider renaming <class 'keras.layers.recurrent.GRUCell'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "625/625 - 829s - loss: 0.5576 - accuracy: 0.6947 - val_loss: 0.4289 - val_accuracy: 0.8014 - lr: 0.0010 - 829s/epoch - 1s/step\n",
      "Epoch 2/50\n",
      "625/625 - 916s - loss: 0.3390 - accuracy: 0.8569 - val_loss: 0.4270 - val_accuracy: 0.8059 - lr: 0.0010 - 916s/epoch - 1s/step\n",
      "Epoch 3/50\n",
      "625/625 - 530s - loss: 0.2152 - accuracy: 0.9151 - val_loss: 0.4932 - val_accuracy: 0.7975 - lr: 0.0010 - 530s/epoch - 848ms/step\n",
      "Epoch 4/50\n",
      "625/625 - 428s - loss: 0.1375 - accuracy: 0.9485 - val_loss: 0.5678 - val_accuracy: 0.7886 - lr: 0.0010 - 428s/epoch - 685ms/step\n",
      "Epoch 5/50\n",
      "625/625 - 416s - loss: 0.0877 - accuracy: 0.9677 - val_loss: 0.6529 - val_accuracy: 0.7814 - lr: 0.0010 - 416s/epoch - 666ms/step\n",
      "Epoch 6/50\n",
      "625/625 - 404s - loss: 0.0595 - accuracy: 0.9779 - val_loss: 0.8498 - val_accuracy: 0.7799 - lr: 0.0010 - 404s/epoch - 647ms/step\n",
      "Epoch 7/50\n",
      "625/625 - 416s - loss: 0.0384 - accuracy: 0.9872 - val_loss: 0.9698 - val_accuracy: 0.7733 - lr: 0.0010 - 416s/epoch - 665ms/step\n",
      "Epoch 8/50\n",
      "625/625 - 411s - loss: 0.0291 - accuracy: 0.9901 - val_loss: 1.2768 - val_accuracy: 0.7699 - lr: 0.0010 - 411s/epoch - 658ms/step\n",
      "Epoch 9/50\n",
      "625/625 - 408s - loss: 0.0220 - accuracy: 0.9928 - val_loss: 1.2863 - val_accuracy: 0.7732 - lr: 0.0010 - 408s/epoch - 653ms/step\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, GlobalAveragePooling1D, GRU, Dropout\n",
    "from keras.callbacks import LearningRateScheduler, ModelCheckpoint\n",
    "\n",
    "# define function for lerning rate schedule \n",
    "def scheduler(epoch, lr): \n",
    "      if epoch < 10:\n",
    "        return lr\n",
    "      else:\n",
    "        return lr * tf.math.exp(-0.1)\n",
    "\n",
    "max_epochs = 50 #number epochs to use\n",
    "\n",
    "model_9 = Sequential()\n",
    "model_9.add(Embedding(max_words, embedding_dim, input_length = max_length))\n",
    "model_9.add(GRU(150, activation='tanh',  return_sequences=True)) # add the GRU layer\n",
    "model_9.add(Flatten())                                          # add the Flatten layer\n",
    "model_9.add(Dense(100, activation='relu'))                       # add the Dense layer\n",
    "model_9.add(Dropout(0.5)) \n",
    "model_9.add(Dense(75, activation='relu'))                       # add the Dense layer\n",
    "model_9.add(Dropout(0.5))                                      # add the Dropout layer\n",
    "model_9.add(Dense(50, activation='relu'))                       # add the Dense layer\n",
    "model_9.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "model_9.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])\n",
    "\n",
    "model_9.summary()\n",
    "\n",
    "early_stopping = EarlyStopping(monitor=\"val_loss\", \n",
    "                               patience=7, \n",
    "                               restore_best_weights=True)\n",
    "\n",
    "save_model_9 = model_9.save(\"/models/model_9\") # saving model\n",
    "\n",
    "# learning rate\n",
    "l_rate = LearningRateScheduler(scheduler)\n",
    "history_9 = model_9.fit(x_train, y_train, \n",
    "                    epochs=max_epochs,\n",
    "                          batch_size=32,\n",
    "                          validation_data=(x_test, y_test),\n",
    "                          callbacks=[early_stopping,l_rate],\n",
    "                          verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "6d46f700",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train accuracy for GRU: 90.34\n",
      "Test accuracy for GRU : 80.48\n"
     ]
    }
   ],
   "source": [
    "# model evaluation\n",
    "train_gru_results =model_9.evaluate(x_train, y_train, verbose=0, batch_size=32)\n",
    "test_gru_results = model_9.evaluate(x_test, y_test, verbose=0, batch_size=32)\n",
    "print(f'Train accuracy for GRU: {train_gru_results[1]*100:0.2f}')\n",
    "print(f'Test accuracy for GRU : {test_gru_results[1]*100:0.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "cf70d149",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.27521688]\n",
      " [0.9515751 ]]\n"
     ]
    }
   ],
   "source": [
    "#check the model\n",
    "sentences = ['lesbian consider father indiana amazing one', \"terrifying server whole heartedly care guest dining experience\"]\n",
    "sequencess = token.texts_to_sequences(sentences)\n",
    "\n",
    "padded = pad_sequences(sequencess, maxlen=max_length, padding=padding_type, truncating=trunc_type)\n",
    "\n",
    "# make a prediction\n",
    "pred_9= model_9.predict(padded)\n",
    "print(pred_9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "9eb3307f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history_9.history['accuracy'], label = \"acc_mod_9\")\n",
    "plt.plot(history_9.history['val_accuracy'], label = \"val_acc_mod_9\")\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "plt.plot(history_9.history['loss'], label = \"loss_mod_9\")\n",
    "plt.plot(history_9.history['val_loss'], label = \"val_loss_mod_9\")\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4cb49fd",
   "metadata": {},
   "source": [
    "GRU & Bidirectional"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "250806df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_24\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " embedding_24 (Embedding)    (None, 100, 64)           640000    \n",
      "                                                                 \n",
      " bidirectional_15 (Bidirecti  (None, 100, 64)          18816     \n",
      " onal)                                                           \n",
      "                                                                 \n",
      " flatten_11 (Flatten)        (None, 6400)              0         \n",
      "                                                                 \n",
      " dense_52 (Dense)            (None, 24)                153624    \n",
      "                                                                 \n",
      " dropout_18 (Dropout)        (None, 24)                0         \n",
      "                                                                 \n",
      " dense_53 (Dense)            (None, 1)                 25        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 812,465\n",
      "Trainable params: 172,465\n",
      "Non-trainable params: 640,000\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as gru_cell_26_layer_call_fn, gru_cell_26_layer_call_and_return_conditional_losses, gru_cell_27_layer_call_fn, gru_cell_27_layer_call_and_return_conditional_losses, gru_cell_26_layer_call_fn while saving (showing 5 of 10). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: /models/model_10\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: /models/model_10\\assets\n",
      "WARNING:absl:<keras.layers.recurrent.GRUCell object at 0x000001D81E3CD0D0> has the same name 'GRUCell' as a built-in Keras object. Consider renaming <class 'keras.layers.recurrent.GRUCell'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
      "WARNING:absl:<keras.layers.recurrent.GRUCell object at 0x000001D81FA45730> has the same name 'GRUCell' as a built-in Keras object. Consider renaming <class 'keras.layers.recurrent.GRUCell'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "625/625 - 145s - loss: 0.6592 - accuracy: 0.6036 - val_loss: 0.6332 - val_accuracy: 0.6359 - lr: 0.0010 - 145s/epoch - 232ms/step\n",
      "Epoch 2/50\n",
      "625/625 - 124s - loss: 0.6321 - accuracy: 0.6403 - val_loss: 0.6185 - val_accuracy: 0.6452 - lr: 0.0010 - 124s/epoch - 198ms/step\n",
      "Epoch 3/50\n",
      "625/625 - 122s - loss: 0.6213 - accuracy: 0.6490 - val_loss: 0.6182 - val_accuracy: 0.6402 - lr: 0.0010 - 122s/epoch - 196ms/step\n",
      "Epoch 4/50\n",
      "625/625 - 123s - loss: 0.6149 - accuracy: 0.6500 - val_loss: 0.6078 - val_accuracy: 0.6512 - lr: 0.0010 - 123s/epoch - 197ms/step\n",
      "Epoch 5/50\n",
      "625/625 - 125s - loss: 0.6079 - accuracy: 0.6517 - val_loss: 0.6094 - val_accuracy: 0.6493 - lr: 0.0010 - 125s/epoch - 200ms/step\n",
      "Epoch 6/50\n",
      "625/625 - 126s - loss: 0.6024 - accuracy: 0.6592 - val_loss: 0.6055 - val_accuracy: 0.6540 - lr: 0.0010 - 126s/epoch - 201ms/step\n",
      "Epoch 7/50\n",
      "625/625 - 125s - loss: 0.5979 - accuracy: 0.6642 - val_loss: 0.5995 - val_accuracy: 0.6578 - lr: 0.0010 - 125s/epoch - 199ms/step\n",
      "Epoch 8/50\n",
      "625/625 - 350s - loss: 0.5938 - accuracy: 0.6658 - val_loss: 0.6004 - val_accuracy: 0.6561 - lr: 0.0010 - 350s/epoch - 560ms/step\n",
      "Epoch 9/50\n",
      "625/625 - 183s - loss: 0.5871 - accuracy: 0.6713 - val_loss: 0.5998 - val_accuracy: 0.6534 - lr: 0.0010 - 183s/epoch - 293ms/step\n",
      "Epoch 10/50\n",
      "625/625 - 73s - loss: 0.5816 - accuracy: 0.6729 - val_loss: 0.5957 - val_accuracy: 0.6580 - lr: 0.0010 - 73s/epoch - 117ms/step\n",
      "Epoch 11/50\n",
      "625/625 - 74s - loss: 0.5775 - accuracy: 0.6775 - val_loss: 0.5965 - val_accuracy: 0.6568 - lr: 9.0484e-04 - 74s/epoch - 119ms/step\n",
      "Epoch 12/50\n",
      "625/625 - 75s - loss: 0.5728 - accuracy: 0.6806 - val_loss: 0.5979 - val_accuracy: 0.6565 - lr: 8.1873e-04 - 75s/epoch - 120ms/step\n",
      "Epoch 13/50\n",
      "625/625 - 71s - loss: 0.5669 - accuracy: 0.6826 - val_loss: 0.5991 - val_accuracy: 0.6607 - lr: 7.4082e-04 - 71s/epoch - 113ms/step\n",
      "Epoch 14/50\n",
      "625/625 - 76s - loss: 0.5632 - accuracy: 0.6885 - val_loss: 0.5964 - val_accuracy: 0.6598 - lr: 6.7032e-04 - 76s/epoch - 122ms/step\n",
      "Epoch 15/50\n",
      "625/625 - 75s - loss: 0.5597 - accuracy: 0.6941 - val_loss: 0.5946 - val_accuracy: 0.6610 - lr: 6.0653e-04 - 75s/epoch - 121ms/step\n",
      "Epoch 16/50\n",
      "625/625 - 75s - loss: 0.5547 - accuracy: 0.6957 - val_loss: 0.5975 - val_accuracy: 0.6625 - lr: 5.4881e-04 - 75s/epoch - 120ms/step\n",
      "Epoch 17/50\n",
      "625/625 - 71s - loss: 0.5523 - accuracy: 0.7007 - val_loss: 0.5980 - val_accuracy: 0.6643 - lr: 4.9659e-04 - 71s/epoch - 114ms/step\n",
      "Epoch 18/50\n",
      "625/625 - 74s - loss: 0.5494 - accuracy: 0.7030 - val_loss: 0.6029 - val_accuracy: 0.6642 - lr: 4.4933e-04 - 74s/epoch - 118ms/step\n",
      "Epoch 19/50\n",
      "625/625 - 74s - loss: 0.5437 - accuracy: 0.7086 - val_loss: 0.6003 - val_accuracy: 0.6666 - lr: 4.0657e-04 - 74s/epoch - 118ms/step\n",
      "Epoch 20/50\n",
      "625/625 - 74s - loss: 0.5433 - accuracy: 0.7097 - val_loss: 0.5982 - val_accuracy: 0.6628 - lr: 3.6788e-04 - 74s/epoch - 119ms/step\n",
      "Epoch 21/50\n",
      "625/625 - 70s - loss: 0.5396 - accuracy: 0.7140 - val_loss: 0.6000 - val_accuracy: 0.6653 - lr: 3.3287e-04 - 70s/epoch - 112ms/step\n",
      "Epoch 22/50\n",
      "625/625 - 46s - loss: 0.5360 - accuracy: 0.7153 - val_loss: 0.5985 - val_accuracy: 0.6693 - lr: 3.0119e-04 - 46s/epoch - 73ms/step\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, GlobalAveragePooling1D, GRU, Dropout, MaxPool1D\n",
    "from keras.callbacks import LearningRateScheduler, ModelCheckpoint\n",
    "\n",
    "# define function for lerning rate schedule \n",
    "def scheduler(epoch, lr): \n",
    "      if epoch < 10:\n",
    "        return lr\n",
    "      else:\n",
    "        return lr * tf.math.exp(-0.1)\n",
    "\n",
    "max_epochs = 50 #number epochs to use\n",
    "\n",
    "model_10 = Sequential()\n",
    "model_10.add(Embedding(max_words, embedding_dim, input_length = max_length, trainable = False,mask_zero=True))\n",
    "model_10.add(Bidirectional(GRU(32, activation='tanh',  return_sequences=True))) # add the GRU layer\n",
    "model_10.add(Flatten())                                        # add the Flatten layer\n",
    "model_10.add(Dense(24, activation='relu'))                       # add the Dense layer\n",
    "model_10.add(Dropout(0.5))                                      # add the Dropout layer\n",
    "model_10.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "model_10.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])\n",
    "\n",
    "model_10.summary()\n",
    "\n",
    "early_stopping = EarlyStopping(monitor=\"val_loss\", \n",
    "                               patience=7, \n",
    "                               restore_best_weights=True)\n",
    "\n",
    "save_model_10 = model_10.save(\"/models/model_10\") # saving model\n",
    "\n",
    "# learning rate\n",
    "l_rate = LearningRateScheduler(scheduler)\n",
    "history_10 = model_10.fit(x_train, y_train, \n",
    "                    epochs=max_epochs,\n",
    "                          batch_size=32,\n",
    "                          validation_data=(x_test, y_test),\n",
    "                          callbacks=[early_stopping,l_rate],\n",
    "                          verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "89a72352",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train accuracy for GRU: 70.92\n",
      "Test accuracy for GRU : 66.10\n"
     ]
    }
   ],
   "source": [
    "# model evaluation\n",
    "train_gru_bid_results =model_10.evaluate(x_train, y_train, verbose=0, batch_size=32)\n",
    "test_gru_bid_results = model_10.evaluate(x_test, y_test, verbose=0, batch_size=32)\n",
    "print(f'Train accuracy for GRU: {train_gru_bid_results[1]*100:0.2f}')\n",
    "print(f'Test accuracy for GRU : {test_gru_bid_results[1]*100:0.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "457841be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.40324685]\n",
      " [0.48671532]]\n"
     ]
    }
   ],
   "source": [
    "#check the model\n",
    "sentences = ['lesbian consider father indiana amazing one', \"terrifying server whole heartedly care guest dining experience\"]\n",
    "sequencess = token.texts_to_sequences(sentences)\n",
    "\n",
    "padded = pad_sequences(sequencess, maxlen=max_length, padding=padding_type, truncating=trunc_type)\n",
    "\n",
    "# make a prediction\n",
    "pred_10= model_10.predict(padded)\n",
    "print(pred_10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "3a30e51b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history_10.history['accuracy'], label = \"acc_mod_10\")\n",
    "plt.plot(history_10.history['val_accuracy'], label = \"val_acc_mod_10\")\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "plt.plot(history_10.history['loss'], label = \"loss_mod_10\")\n",
    "plt.plot(history_10.history['val_loss'], label = \"val_loss_mod_10\")\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "7fc1d366",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GlobalAv1\n",
      "train_accuracy: 52.31\n",
      "test_accuracy: 52.49\n",
      "GlobalAv2\n",
      "train_accuracy: 85.90\n",
      "test_accuracy: 79.84\n",
      "Conv_GlobAv1\n",
      "train_accuracy: 93.59\n",
      "test_accuracy: 79.88\n",
      "Conv_GlobAv2\n",
      "train_accuracy: 90.61\n",
      "test_accuracy: 79.28\n",
      "SRNN\n",
      "train_accuracy: 52.31\n",
      "test_accuracy: 52.49\n",
      "SRNN_Bidirectional\n",
      "train_accuracy: 80.92\n",
      "test_accuracy: 70.32\n",
      "LSTM\n",
      "train_accuracy: 92.80\n",
      "test_accuracy: 80.51\n",
      "GRU\n",
      "train_accuracy: 90.50\n",
      "test_accuracy: 80.64\n",
      "GRU_Scheduler\n",
      "train_accuracy: 93.51\n",
      "test_accuracy: 80.59\n",
      "GRU_Bidirectional\n",
      "train_accuracy: 64.53\n",
      "test_accuracy: 63.96\n"
     ]
    }
   ],
   "source": [
    "models = []\n",
    "models.append(('GlobalAv1', model_1))\n",
    "models.append(('GlobalAv2', model_2))\n",
    "models.append(('Conv_GlobAv1', model_3))\n",
    "models.append(('Conv_GlobAv2', model_4))\n",
    "models.append(('SRNN', model_5))\n",
    "models.append(('SRNN_Bidirectional', model_6))\n",
    "models.append(('LSTM', model_7))\n",
    "models.append(('GRU', model_8))\n",
    "models.append(('GRU_Scheduler', model_9))\n",
    "models.append(('GRU_Bidirectional', model_10))\n",
    "\n",
    "\n",
    "train_accuracy = []\n",
    "test_accuracy = []\n",
    "for name, model in models:\n",
    "    print(name)\n",
    "    print(f'train_accuracy: {model.evaluate(x_train, y_train, verbose=0, batch_size=32)[1]*100:0.2f}')\n",
    "    print(f'test_accuracy: {model.evaluate(x_test, y_test, verbose=0, batch_size=32)[1]*100:0.2f}')\n",
    "    \n",
    "    train_accuracy.append(f' {model.evaluate(x_train, y_train, verbose=0, batch_size=32)[1]*100:0.2f}')\n",
    "    test_accuracy.append(f' {model.evaluate(x_test, y_test, verbose=0, batch_size=32)[1]*100:0.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "780fa2d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>test_accuracy</th>\n",
       "      <th>train_accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>GRU</td>\n",
       "      <td>80.64</td>\n",
       "      <td>90.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>GRU_Scheduler</td>\n",
       "      <td>80.59</td>\n",
       "      <td>93.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>LSTM</td>\n",
       "      <td>80.51</td>\n",
       "      <td>92.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Conv_GlobAv1</td>\n",
       "      <td>79.88</td>\n",
       "      <td>93.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GlobalAv2</td>\n",
       "      <td>79.84</td>\n",
       "      <td>85.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Conv_GlobAv2</td>\n",
       "      <td>79.28</td>\n",
       "      <td>90.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>SRNN_Bidirectional</td>\n",
       "      <td>70.32</td>\n",
       "      <td>80.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>GRU_Bidirectional</td>\n",
       "      <td>63.96</td>\n",
       "      <td>64.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GlobalAv1</td>\n",
       "      <td>52.49</td>\n",
       "      <td>52.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SRNN</td>\n",
       "      <td>52.49</td>\n",
       "      <td>52.31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Model test_accuracy train_accuracy\n",
       "7                 GRU         80.64          90.50\n",
       "8       GRU_Scheduler         80.59          93.51\n",
       "6                LSTM         80.51          92.80\n",
       "2        Conv_GlobAv1         79.88          93.59\n",
       "1           GlobalAv2         79.84          85.90\n",
       "3        Conv_GlobAv2         79.28          90.61\n",
       "5  SRNN_Bidirectional         70.32          80.92\n",
       "9   GRU_Bidirectional         63.96          64.53\n",
       "0           GlobalAv1         52.49          52.31\n",
       "4                SRNN         52.49          52.31"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creat the Data Frame for accuracy in models\n",
    "import pandas as pd\n",
    "accs = {'test_accuracy': test_accuracy,\n",
    "          'train_accuracy': train_accuracy\n",
    "    }\n",
    "df = pd.DataFrame(data=accs)\n",
    "df.insert(loc=0, column='Model', value=['GlobalAv1','GlobalAv2', 'Conv_GlobAv1', 'Conv_GlobAv2','SRNN',\n",
    "                                        'SRNN_Bidirectional','LSTM','GRU','GRU_Scheduler','GRU_Bidirectional'])\n",
    "df.sort_values('test_accuracy',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "028e9815",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "acc = {\n",
    "     'test_accuracy': test_accuracy\n",
    "    }\n",
    "\n",
    "df1 = pd.DataFrame(data=acc)\n",
    "df1.insert(loc=0, column='Model', value=['GlobalAv1','GlobalAv2', 'Conv_GlobAv1', 'Conv_GlobAv2','SRNN',\n",
    "                                        'SRNN_Bidirectional','LSTM','GRU','GRU_Scheduler','GRU_Bidirectional'])  \n",
    "\n",
    "#df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "ded1a675",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1['test_accuracy'] = df1['test_accuracy'].astype('float')\n",
    "\n",
    "plt.figure(figsize = (16,10)) #plot the accuracy scores\n",
    "sns.barplot(x=df1['Model'], y=df1['test_accuracy'], data=df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "b1a3a7b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_GRU = model_8.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "7874ee1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix,classification_report\n",
    "conf_mtx=confusion_matrix(pred_GRU.round(), y_test)\n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "sns.heatmap(conf_mtx,annot=True,fmt='d',cmap='Blues')\n",
    "plt.title(\"0 - Non_sarcastic     1 - Sarcastic\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "f6c60717",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8063580461770506"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(3794+3156)/(3794+3156+939+730) #check accuracy with confusion matrix result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6355e1e4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}