NLP_sentiment.ipynb

{
 "nbformat": 4,
 "nbformat_minor": 0,
 "metadata": {
  "colab": {
   "provenance": [],
   "gpuType": "T4"
  },
  "kernelspec": {
   "name": "python3",
   "language": "python",
   "display_name": "Python 3 (ipykernel)"
  },
  "language_info": {
   "name": "python"
  },
  "accelerator": "GPU"
 },
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# Import packages"
   ],
   "metadata": {
    "id": "KK7cgNMRftMM"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "import numpy as np\n",
    "import math\n",
    "import re\n",
    "import pandas as pd\n",
    "from bs4 import BeautifulSoup\n",
    "#from google.colab import drive\n",
    "import pickle\n",
    "import lxml"
   ],
   "metadata": {
    "id": "WeQeHuFpfvIU",
    "ExecuteTime": {
     "end_time": "2024-02-12T15:54:54.138041700Z",
     "start_time": "2024-02-12T15:54:53.812461600Z"
    }
   },
   "execution_count": 1,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ALITEMP\\AppData\\Local\\Temp\\ipykernel_15740\\772563736.py:4: DeprecationWarning: \n",
      "Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),\n",
      "(to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)\n",
      "but was not found to be installed on your system.\n",
      "If this would cause problems for you,\n",
      "please provide us feedback at https://github.com/pandas-dev/pandas/issues/54466\n",
      "        \n",
      "  import pandas as pd\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras import layers\n",
    "from tensorflow.keras.preprocessing.text import Tokenizer\n"
   ],
   "metadata": {
    "id": "ojoSB8r1gSZl",
    "ExecuteTime": {
     "end_time": "2024-02-12T15:55:02.604409400Z",
     "start_time": "2024-02-12T15:55:00.023898600Z"
    }
   },
   "execution_count": 2,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From D:\\Coding Projects\\NLP-Project-Sentiment\\lib\\site-packages\\keras\\src\\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Load files"
   ],
   "metadata": {
    "id": "4zlEVP0nlaju"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "cols =['sentiment','id','date','query','user','text']\n",
    "\n",
    "data = pd.read_csv(\n",
    "    \"NLP-Project-Sentiment/training.1600000.processed.noemoticon.csv\",\n",
    "    header=None,\n",
    "    names=cols,\n",
    "    engine='python',\n",
    "    encoding='latin1'\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-12T15:55:10.390381500Z",
     "start_time": "2024-02-12T15:55:05.132592500Z"
    }
   }
  },
  {
   "cell_type": "code",
   "source": [
    "data.head(3)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 195
    },
    "id": "Z9jPx71rm64o",
    "outputId": "29df8392-e201-4b9c-e788-7d8e774828cf",
    "ExecuteTime": {
     "end_time": "2024-02-12T15:55:10.410345400Z",
     "start_time": "2024-02-12T15:55:10.391381900Z"
    }
   },
   "execution_count": 4,
   "outputs": [
    {
     "data": {
      "text/plain": "   sentiment          id                          date     query  \\\n0          0  1467810369  Mon Apr 06 22:19:45 PDT 2009  NO_QUERY   \n1          0  1467810672  Mon Apr 06 22:19:49 PDT 2009  NO_QUERY   \n2          0  1467810917  Mon Apr 06 22:19:53 PDT 2009  NO_QUERY   \n\n              user                                               text  \n0  _TheSpecialOne_  @switchfoot http://twitpic.com/2y1zl - Awww, t...  \n1    scotthamilton  is upset that he can't update his Facebook by ...  \n2         mattycus  @Kenichan I dived many times for the ball. Man...  ",
      "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>sentiment</th>\n      <th>id</th>\n      <th>date</th>\n      <th>query</th>\n      <th>user</th>\n      <th>text</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>1467810369</td>\n      <td>Mon Apr 06 22:19:45 PDT 2009</td>\n      <td>NO_QUERY</td>\n      <td>_TheSpecialOne_</td>\n      <td>@switchfoot http://twitpic.com/2y1zl - Awww, t...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>1467810672</td>\n      <td>Mon Apr 06 22:19:49 PDT 2009</td>\n      <td>NO_QUERY</td>\n      <td>scotthamilton</td>\n      <td>is upset that he can't update his Facebook by ...</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>1467810917</td>\n      <td>Mon Apr 06 22:19:53 PDT 2009</td>\n      <td>NO_QUERY</td>\n      <td>mattycus</td>\n      <td>@Kenichan I dived many times for the ball. Man...</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Pre process data"
   ],
   "metadata": {
    "id": "PCX4ZzQLnORI"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "data.drop(['id','date','query','user'],axis=1,inplace=True)"
   ],
   "metadata": {
    "id": "tjBgQKaZnRD4",
    "ExecuteTime": {
     "end_time": "2024-02-12T15:55:10.460321100Z",
     "start_time": "2024-02-12T15:55:10.406343700Z"
    }
   },
   "execution_count": 5,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "data.head()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 423
    },
    "id": "0BfY30g5ngsS",
    "outputId": "77aafb35-fb41-49a8-fb97-c48aa5fc66be",
    "ExecuteTime": {
     "end_time": "2024-02-12T15:55:10.506801900Z",
     "start_time": "2024-02-12T15:55:10.437878200Z"
    }
   },
   "execution_count": 6,
   "outputs": [
    {
     "data": {
      "text/plain": "   sentiment                                               text\n0          0  @switchfoot http://twitpic.com/2y1zl - Awww, t...\n1          0  is upset that he can't update his Facebook by ...\n2          0  @Kenichan I dived many times for the ball. Man...\n3          0    my whole body feels itchy and like its on fire \n4          0  @nationwideclass no, it's not behaving at all....",
      "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>sentiment</th>\n      <th>text</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>@switchfoot http://twitpic.com/2y1zl - Awww, t...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>is upset that he can't update his Facebook by ...</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>@Kenichan I dived many times for the ball. Man...</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>my whole body feels itchy and like its on fire</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>@nationwideclass no, it's not behaving at all....</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "def cleanText(text):\n",
    "  text = BeautifulSoup(text,'html.parser').get_text()\n",
    "  text = re.sub(r'@[A-za-z0-9]+',\" \",text)\n",
    "  text = re.sub(r'https?://[A-za-z0-9./]+',' ',text)\n",
    "  text = re.sub(r'[^a-zA-Z.!?]',' ',text)\n",
    "  text = re.sub(r' +',\" \",text)\n",
    "  return text"
   ],
   "metadata": {
    "id": "n5ssaQU6nmOb",
    "ExecuteTime": {
     "end_time": "2024-02-12T15:55:11.456792200Z",
     "start_time": "2024-02-12T15:55:11.433545600Z"
    }
   },
   "execution_count": 7,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Clean the text"
   ],
   "metadata": {
    "id": "skiHRm4QE7O1"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "data_clean = [cleanText(text) for text in data.text]"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "cXJvm6heqHhk",
    "outputId": "bd2c627c-e715-4141-d2ab-6921cd636ed0",
    "ExecuteTime": {
     "end_time": "2024-02-12T15:55:53.744370500Z",
     "start_time": "2024-02-12T15:55:16.567663900Z"
    }
   },
   "execution_count": 8,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ALITEMP\\AppData\\Local\\Temp\\ipykernel_15740\\1337334217.py:2: MarkupResemblesLocatorWarning: The input looks more like a filename than markup. You may want to open this file and pass the filehandle into Beautiful Soup.\n",
      "  text = BeautifulSoup(text,'html.parser').get_text()\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "data": {
      "text/plain": "[' Awww that s a bummer. You shoulda got David Carr of Third Day to do it. D',\n 'is upset that he can t update his Facebook by texting it... and might cry as a result School today also. Blah!',\n ' I dived many times for the ball. Managed to save The rest go out of bounds',\n 'my whole body feels itchy and like its on fire ',\n ' no it s not behaving at all. i m mad. why am i here? because I can t see you all over there. ']"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_clean[:5]"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-12T15:55:53.760854300Z",
     "start_time": "2024-02-12T15:55:53.744723400Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Clean Labels\n",
    "0 is Positive\n",
    "\n",
    "1 is Negative"
   ],
   "metadata": {
    "id": "t8LasNyREgLf"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "data_labels = data.sentiment.values\n",
    "data_labels[data_labels==4] = 1\n",
    "set(data_labels)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DR6oMbF8B9Q3",
    "outputId": "a31fc3ac-7904-4c3f-a7ae-3cc179e5628e",
    "ExecuteTime": {
     "end_time": "2024-02-12T15:55:53.822860Z",
     "start_time": "2024-02-12T15:55:53.760854300Z"
    }
   },
   "execution_count": 10,
   "outputs": [
    {
     "data": {
      "text/plain": "{0, 1}"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Tokenization"
   ],
   "metadata": {
    "id": "9ZCXKaywFNlN"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [],
   "source": [
    "# Load the tokenizer from a file\n",
    "with open('NLP-Project-Sentiment/tokenizer.pickle', 'rb') as handle:\n",
    "    tokenizer = pickle.load(handle)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-12T15:55:53.944663100Z",
     "start_time": "2024-02-12T15:55:53.823860100Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "tokenizer = Tokenizer(num_words=2**16, oov_token='<OOV>')\n",
    "tokenizer.fit_on_texts(data_clean)\n",
    "# Save the tokenizer to a file\n",
    "with open('NLP-Project-Sentiment/tokenizer.pickle', 'wb') as handle:\n",
    "    pickle.dump(tokenizer, handle, protocol=pickle.HIGHEST_PROTOCOL)\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "source": [
    "sequences = tokenizer.texts_to_sequences(data_clean)\n",
    "data_inputs =sequences"
   ],
   "metadata": {
    "id": "KMu91iRVGP9b",
    "ExecuteTime": {
     "end_time": "2024-02-12T15:56:04.298446500Z",
     "start_time": "2024-02-12T15:55:54.069873800Z"
    }
   },
   "execution_count": 12,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Padding\n",
    "Add 0 for all sentences to match maximum length of each sentence after it was tokenized"
   ],
   "metadata": {
    "id": "oD3bOfdlH9Uf"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "MAX_LENGTH = max([len(sentence) for sentence in data_inputs])\n",
    "data_inputs= tf.keras.preprocessing.sequence.pad_sequences(\n",
    "    data_inputs,\n",
    "    value=0,\n",
    "    padding=\"post\",\n",
    "    maxlen=MAX_LENGTH\n",
    ")"
   ],
   "metadata": {
    "id": "q3LxFLkbH_P_",
    "ExecuteTime": {
     "end_time": "2024-02-12T15:56:06.254457600Z",
     "start_time": "2024-02-12T15:56:04.299445700Z"
    }
   },
   "execution_count": 13,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "print(MAX_LENGTH)\n",
    "data_inputs[:5]"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "b94TAQ5pM6do",
    "outputId": "2b3a620a-a38b-4893-b7ec-b39b623feaed",
    "ExecuteTime": {
     "end_time": "2024-02-12T15:56:06.274446900Z",
     "start_time": "2024-02-12T15:56:06.256457800Z"
    }
   },
   "execution_count": 14,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "52\n"
     ]
    },
    {
     "data": {
      "text/plain": "array([[  445,    17,    13,     5,  1174,     9,  3429,    50,   836,\n         9439,    15,  1864,    34,     3,    43,     7,   140,     0,\n            0,     0,     0,     0,     0,     0,     0,     0,     0,\n            0,     0,     0,     0,     0,     0,     0,     0,     0,\n            0,     0,     0,     0,     0,     0,     0,     0,     0,\n            0,     0,     0,     0,     0,     0,     0],\n       [   10,   781,    17,    85,    31,    14,   544,   184,   532,\n          124,  1992,     7,     8,   290,   528,    84,     5,  2301,\n          144,    42,   266,  1117,     0,     0,     0,     0,     0,\n            0,     0,     0,     0,     0,     0,     0,     0,     0,\n            0,     0,     0,     0,     0,     0,     0,     0,     0,\n            0,     0,     0,     0,     0,     0,     0],\n       [    2, 51831,   314,   351,    12,     4,  1228,  1659,     3,\n          896,     4,   468,    41,    36,    15, 22589,     0,     0,\n            0,     0,     0,     0,     0,     0,     0,     0,     0,\n            0,     0,     0,     0,     0,     0,     0,     0,     0,\n            0,     0,     0,     0,     0,     0,     0,     0,     0,\n            0,     0,     0,     0,     0,     0,     0],\n       [    6,   427,   801,   480,  2868,     8,    38,    69,    16,\n         1143,     0,     0,     0,     0,     0,     0,     0,     0,\n            0,     0,     0,     0,     0,     0,     0,     0,     0,\n            0,     0,     0,     0,     0,     0,     0,     0,     0,\n            0,     0,     0,     0,     0,     0,     0,     0,     0,\n            0,     0,     0,     0,     0,     0,     0],\n       [   40,     7,    13,    27, 10043,    26,    35,     2,    21,\n          594,   111,    54,     2,    87,   208,     2,    31,    14,\n           66,     9,    35,   132,    68,     0,     0,     0,     0,\n            0,     0,     0,     0,     0,     0,     0,     0,     0,\n            0,     0,     0,     0,     0,     0,     0,     0,     0,\n            0,     0,     0,     0,     0,     0,     0]])"
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Split Data into Train/Test"
   ],
   "metadata": {
    "id": "aHvpTyJGJM1q"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "test_idx = np.random.randint(0,800000,8000)\n",
    "test_idx = np.concatenate((test_idx,test_idx+800000))"
   ],
   "metadata": {
    "id": "2npuFQA0JPK6",
    "ExecuteTime": {
     "end_time": "2024-02-12T15:56:06.297775500Z",
     "start_time": "2024-02-12T15:56:06.271658500Z"
    }
   },
   "execution_count": 15,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "test_inputs = data_inputs[test_idx]\n",
    "test_labels = data_labels[test_idx]"
   ],
   "metadata": {
    "id": "PGHlSIRFKx5C",
    "ExecuteTime": {
     "end_time": "2024-02-12T15:56:06.308034800Z",
     "start_time": "2024-02-12T15:56:06.287059300Z"
    }
   },
   "execution_count": 16,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "train_inputs = np.delete(data_inputs,test_idx,axis=0)\n",
    "train_labels = np.delete(data_labels,test_idx)"
   ],
   "metadata": {
    "id": "eFbZArAhLBoU",
    "ExecuteTime": {
     "end_time": "2024-02-12T15:56:06.383113500Z",
     "start_time": "2024-02-12T15:56:06.302775800Z"
    }
   },
   "execution_count": 17,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Building the model"
   ],
   "metadata": {
    "id": "YnDJjhzePSU3"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "class DeepCNN(tf.keras.Model):\n",
    "  def __init__(self,\n",
    "               vocab_size,\n",
    "               emb_dim=128,\n",
    "               nb_filters=50,\n",
    "               FFN_units=512,\n",
    "               nb_classes=2,\n",
    "               dropout_rate=0.1,\n",
    "               training=False,\n",
    "               name=\"DeepCNN\",\n",
    "               ):\n",
    "    super(DeepCNN,self).__init__(name=name)\n",
    "    self.embedding= layers.Embedding(vocab_size,\n",
    "                                      emb_dim)\n",
    "    self.bigram = layers.Conv1D(filters=nb_filters,\n",
    "                                kernel_size=2,\n",
    "                                padding=\"valid\",\n",
    "                                activation=\"relu\")\n",
    "    self.pool_1= layers.GlobalMaxPool1D()\n",
    "    self.trigram = layers.Conv1D(filters=nb_filters,\n",
    "                                kernel_size=3,\n",
    "                                padding=\"valid\",\n",
    "                                activation=\"relu\")\n",
    "    self.pool_2= layers.GlobalMaxPool1D()\n",
    "    self.quadgram = layers.Conv1D(filters=nb_filters,\n",
    "                                kernel_size=4,\n",
    "                                padding=\"valid\",\n",
    "                                activation=\"relu\")\n",
    "    self.pool_3= layers.GlobalMaxPool1D()\n",
    "    self.dense_1 = layers.Dense(units=FFN_units, activation=\"relu\")\n",
    "    self.dropout = layers.Dropout(rate = dropout_rate)\n",
    "    if nb_classes == 2:\n",
    "      self.last_dense = layers.Dense(units=1,activation=\"sigmoid\")\n",
    "    else:\n",
    "      # You will need to get the maximum probablity later\n",
    "      self.last_dense = layers.Dense(units=nb_classes,activation=\"softmax\")\n",
    "\n",
    "  def call(self,inputs,training):\n",
    "    x = self.embedding(inputs)\n",
    "    x_1 = self.bigram(x)\n",
    "    x_1 = self.pool_1(x_1)\n",
    "    x_2 = self.trigram(x)\n",
    "    x_2 = self.pool_2(x_2)\n",
    "    x_3 = self.quadgram(x)\n",
    "    x_3 = self.pool_3(x_3)\n",
    "\n",
    "    merged = tf.concat([x_1,x_2,x_3],axis=-1) # something like this (batchsize, 3*nb_filters)\n",
    "    merged = self.dense_1(merged)\n",
    "    merged = self.dropout(merged,training)\n",
    "    output = self.last_dense(merged)\n",
    "    return output\n",
    "\n"
   ],
   "metadata": {
    "id": "GUMTOPm8PVQe",
    "ExecuteTime": {
     "end_time": "2024-02-12T15:56:06.395892800Z",
     "start_time": "2024-02-12T15:56:06.383113500Z"
    }
   },
   "execution_count": 18,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Where the magic happens"
   ],
   "metadata": {
    "id": "UcvatzMSTT2B"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Config"
   ],
   "metadata": {
    "id": "ZYEYjmmBTYnC"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "VOCAB_SIZE = len(tokenizer.word_index)\n",
    "\n",
    "EMB_DIM = 200\n",
    "NB_FILTERS = 100\n",
    "FFN_UNITS = 256\n",
    "NB_CLASSES= len(set(train_labels))\n",
    "\n",
    "DROUPOUT_RATE = 0.2\n",
    "\n",
    "BATCH_SIZE = 32\n",
    "NB_EPOCHS = 1"
   ],
   "metadata": {
    "id": "Cl-ylproTwCj",
    "ExecuteTime": {
     "end_time": "2024-02-12T15:56:06.457101300Z",
     "start_time": "2024-02-12T15:56:06.395892800Z"
    }
   },
   "execution_count": 19,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Training"
   ],
   "metadata": {
    "id": "blDHZQcIULyJ"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "deepCNN = DeepCNN(vocab_size=VOCAB_SIZE,\n",
    "                  emb_dim=EMB_DIM,\n",
    "                  nb_filters=NB_FILTERS,\n",
    "                  FFN_units=FFN_UNITS,\n",
    "                  nb_classes=NB_CLASSES,\n",
    "                  dropout_rate=DROUPOUT_RATE,\n",
    "                  )"
   ],
   "metadata": {
    "id": "xGqpP1v-UNVc",
    "ExecuteTime": {
     "end_time": "2024-02-12T15:56:06.536340500Z",
     "start_time": "2024-02-12T15:56:06.458100800Z"
    }
   },
   "execution_count": 20,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From D:\\Coding Projects\\NLP-Project-Sentiment\\lib\\site-packages\\keras\\src\\backend.py:873: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "if NB_CLASSES ==2:\n",
    "  deepCNN.compile(loss=\"binary_crossentropy\",\n",
    "                  optimizer=\"adam\",\n",
    "                  metrics=[\"accuracy\"]\n",
    "                  )\n",
    "else:\n",
    "  deepCNN.compiler(loss=\"sparse_categorical_crossentropy\",\n",
    "                   optimizer=\"adam\",\n",
    "                   metrics=[\"sparse_categorical_accuracy\"]\n",
    "                   )"
   ],
   "metadata": {
    "id": "kGDOa6OhU5ji",
    "ExecuteTime": {
     "end_time": "2024-02-12T15:56:06.580300900Z",
     "start_time": "2024-02-12T15:56:06.535340300Z"
    }
   },
   "execution_count": 21,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From D:\\Coding Projects\\NLP-Project-Sentiment\\lib\\site-packages\\keras\\src\\optimizers\\__init__.py:309: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Latest Checkpoint restored\n"
     ]
    }
   ],
   "source": [
    "checkpoint_path = \"NLP-Project-Sentiment\"\n",
    "ckpt = tf.train.Checkpoint(deepCNN=deepCNN)\n",
    "ckpt_manager=tf.train.CheckpointManager(ckpt,checkpoint_path,max_to_keep=1)\n",
    "\n",
    "if(ckpt_manager.latest_checkpoint):\n",
    "  ckpt.restore(ckpt_manager.latest_checkpoint)\n",
    "  print(\"Latest Checkpoint restored\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-12T15:56:06.591035100Z",
     "start_time": "2024-02-12T15:56:06.566765600Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-12T15:56:06.600770600Z",
     "start_time": "2024-02-12T15:56:06.581299800Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.15.0\n",
      "Num GPUs Available:  [PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU')]\n"
     ]
    }
   ],
   "source": [
    "print(tf.__version__)\n",
    "print(\"Num GPUs Available: \", tf.config.experimental.list_physical_devices())"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-12T15:56:06.652654500Z",
     "start_time": "2024-02-12T15:56:06.596770400Z"
    }
   }
  },
  {
   "cell_type": "code",
   "source": [
    "deepCNN.fit(train_inputs,train_labels,batch_size=BATCH_SIZE,epochs=NB_EPOCHS)\n",
    "ckpt_manager.save()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ifcsLX_jWX7V",
    "outputId": "aefb4257-42ea-479b-be62-000e9da52f52",
    "ExecuteTime": {
     "end_time": "2024-02-12T15:56:30.352330Z",
     "start_time": "2024-02-12T15:56:14.165588100Z"
    }
   },
   "execution_count": 24,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From D:\\Coding Projects\\NLP-Project-Sentiment\\lib\\site-packages\\keras\\src\\utils\\tf_utils.py:492: The name tf.ragged.RaggedTensorValue is deprecated. Please use tf.compat.v1.ragged.RaggedTensorValue instead.\n",
      "WARNING:tensorflow:From D:\\Coding Projects\\NLP-Project-Sentiment\\lib\\site-packages\\keras\\src\\engine\\base_layer_utils.py:384: The name tf.executing_eagerly_outside_functions is deprecated. Please use tf.compat.v1.executing_eagerly_outside_functions instead.\n",
      "  113/49503 [..............................] - ETA: 1:44:34 - loss: 0.1284 - accuracy: 0.9483"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mKeyboardInterrupt\u001B[0m                         Traceback (most recent call last)",
      "Cell \u001B[1;32mIn[24], line 1\u001B[0m\n\u001B[1;32m----> 1\u001B[0m \u001B[43mdeepCNN\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfit\u001B[49m\u001B[43m(\u001B[49m\u001B[43mtrain_inputs\u001B[49m\u001B[43m,\u001B[49m\u001B[43mtrain_labels\u001B[49m\u001B[43m,\u001B[49m\u001B[43mbatch_size\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mBATCH_SIZE\u001B[49m\u001B[43m,\u001B[49m\u001B[43mepochs\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mNB_EPOCHS\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m      2\u001B[0m ckpt_manager\u001B[38;5;241m.\u001B[39msave()\n",
      "File \u001B[1;32mD:\\Coding Projects\\NLP-Project-Sentiment\\lib\\site-packages\\keras\\src\\utils\\traceback_utils.py:65\u001B[0m, in \u001B[0;36mfilter_traceback.<locals>.error_handler\u001B[1;34m(*args, **kwargs)\u001B[0m\n\u001B[0;32m     63\u001B[0m filtered_tb \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[0;32m     64\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m---> 65\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m fn(\u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[0;32m     66\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mException\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m e:\n\u001B[0;32m     67\u001B[0m     filtered_tb \u001B[38;5;241m=\u001B[39m _process_traceback_frames(e\u001B[38;5;241m.\u001B[39m__traceback__)\n",
      "File \u001B[1;32mD:\\Coding Projects\\NLP-Project-Sentiment\\lib\\site-packages\\keras\\src\\engine\\training.py:1807\u001B[0m, in \u001B[0;36mModel.fit\u001B[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001B[0m\n\u001B[0;32m   1799\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m tf\u001B[38;5;241m.\u001B[39mprofiler\u001B[38;5;241m.\u001B[39mexperimental\u001B[38;5;241m.\u001B[39mTrace(\n\u001B[0;32m   1800\u001B[0m     \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mtrain\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[0;32m   1801\u001B[0m     epoch_num\u001B[38;5;241m=\u001B[39mepoch,\n\u001B[1;32m   (...)\u001B[0m\n\u001B[0;32m   1804\u001B[0m     _r\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m1\u001B[39m,\n\u001B[0;32m   1805\u001B[0m ):\n\u001B[0;32m   1806\u001B[0m     callbacks\u001B[38;5;241m.\u001B[39mon_train_batch_begin(step)\n\u001B[1;32m-> 1807\u001B[0m     tmp_logs \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mtrain_function\u001B[49m\u001B[43m(\u001B[49m\u001B[43miterator\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m   1808\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m data_handler\u001B[38;5;241m.\u001B[39mshould_sync:\n\u001B[0;32m   1809\u001B[0m         context\u001B[38;5;241m.\u001B[39masync_wait()\n",
      "File \u001B[1;32mD:\\Coding Projects\\NLP-Project-Sentiment\\lib\\site-packages\\tensorflow\\python\\util\\traceback_utils.py:150\u001B[0m, in \u001B[0;36mfilter_traceback.<locals>.error_handler\u001B[1;34m(*args, **kwargs)\u001B[0m\n\u001B[0;32m    148\u001B[0m filtered_tb \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[0;32m    149\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m--> 150\u001B[0m   \u001B[38;5;28;01mreturn\u001B[39;00m fn(\u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[0;32m    151\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mException\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m e:\n\u001B[0;32m    152\u001B[0m   filtered_tb \u001B[38;5;241m=\u001B[39m _process_traceback_frames(e\u001B[38;5;241m.\u001B[39m__traceback__)\n",
      "File \u001B[1;32mD:\\Coding Projects\\NLP-Project-Sentiment\\lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\polymorphic_function.py:832\u001B[0m, in \u001B[0;36mFunction.__call__\u001B[1;34m(self, *args, **kwds)\u001B[0m\n\u001B[0;32m    829\u001B[0m compiler \u001B[38;5;241m=\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mxla\u001B[39m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_jit_compile \u001B[38;5;28;01melse\u001B[39;00m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mnonXla\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m    831\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m OptionalXlaContext(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_jit_compile):\n\u001B[1;32m--> 832\u001B[0m   result \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_call(\u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwds)\n\u001B[0;32m    834\u001B[0m new_tracing_count \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mexperimental_get_tracing_count()\n\u001B[0;32m    835\u001B[0m without_tracing \u001B[38;5;241m=\u001B[39m (tracing_count \u001B[38;5;241m==\u001B[39m new_tracing_count)\n",
      "File \u001B[1;32mD:\\Coding Projects\\NLP-Project-Sentiment\\lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\polymorphic_function.py:868\u001B[0m, in \u001B[0;36mFunction._call\u001B[1;34m(self, *args, **kwds)\u001B[0m\n\u001B[0;32m    865\u001B[0m   \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_lock\u001B[38;5;241m.\u001B[39mrelease()\n\u001B[0;32m    866\u001B[0m   \u001B[38;5;66;03m# In this case we have created variables on the first call, so we run the\u001B[39;00m\n\u001B[0;32m    867\u001B[0m   \u001B[38;5;66;03m# defunned version which is guaranteed to never create variables.\u001B[39;00m\n\u001B[1;32m--> 868\u001B[0m   \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mtracing_compilation\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcall_function\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m    869\u001B[0m \u001B[43m      \u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mkwds\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_no_variable_creation_config\u001B[49m\n\u001B[0;32m    870\u001B[0m \u001B[43m  \u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    871\u001B[0m \u001B[38;5;28;01melif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_variable_creation_config \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[0;32m    872\u001B[0m   \u001B[38;5;66;03m# Release the lock early so that multiple threads can perform the call\u001B[39;00m\n\u001B[0;32m    873\u001B[0m   \u001B[38;5;66;03m# in parallel.\u001B[39;00m\n\u001B[0;32m    874\u001B[0m   \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_lock\u001B[38;5;241m.\u001B[39mrelease()\n",
      "File \u001B[1;32mD:\\Coding Projects\\NLP-Project-Sentiment\\lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\tracing_compilation.py:139\u001B[0m, in \u001B[0;36mcall_function\u001B[1;34m(args, kwargs, tracing_options)\u001B[0m\n\u001B[0;32m    137\u001B[0m bound_args \u001B[38;5;241m=\u001B[39m function\u001B[38;5;241m.\u001B[39mfunction_type\u001B[38;5;241m.\u001B[39mbind(\u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[0;32m    138\u001B[0m flat_inputs \u001B[38;5;241m=\u001B[39m function\u001B[38;5;241m.\u001B[39mfunction_type\u001B[38;5;241m.\u001B[39munpack_inputs(bound_args)\n\u001B[1;32m--> 139\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mfunction\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_call_flat\u001B[49m\u001B[43m(\u001B[49m\u001B[43m  \u001B[49m\u001B[38;5;66;43;03m# pylint: disable=protected-access\u001B[39;49;00m\n\u001B[0;32m    140\u001B[0m \u001B[43m    \u001B[49m\u001B[43mflat_inputs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcaptured_inputs\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mfunction\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcaptured_inputs\u001B[49m\n\u001B[0;32m    141\u001B[0m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[1;32mD:\\Coding Projects\\NLP-Project-Sentiment\\lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\concrete_function.py:1323\u001B[0m, in \u001B[0;36mConcreteFunction._call_flat\u001B[1;34m(self, tensor_inputs, captured_inputs)\u001B[0m\n\u001B[0;32m   1319\u001B[0m possible_gradient_type \u001B[38;5;241m=\u001B[39m gradients_util\u001B[38;5;241m.\u001B[39mPossibleTapeGradientTypes(args)\n\u001B[0;32m   1320\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m (possible_gradient_type \u001B[38;5;241m==\u001B[39m gradients_util\u001B[38;5;241m.\u001B[39mPOSSIBLE_GRADIENT_TYPES_NONE\n\u001B[0;32m   1321\u001B[0m     \u001B[38;5;129;01mand\u001B[39;00m executing_eagerly):\n\u001B[0;32m   1322\u001B[0m   \u001B[38;5;66;03m# No tape is watching; skip to running the function.\u001B[39;00m\n\u001B[1;32m-> 1323\u001B[0m   \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_inference_function\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcall_preflattened\u001B[49m\u001B[43m(\u001B[49m\u001B[43margs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m   1324\u001B[0m forward_backward \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_select_forward_and_backward_functions(\n\u001B[0;32m   1325\u001B[0m     args,\n\u001B[0;32m   1326\u001B[0m     possible_gradient_type,\n\u001B[0;32m   1327\u001B[0m     executing_eagerly)\n\u001B[0;32m   1328\u001B[0m forward_function, args_with_tangents \u001B[38;5;241m=\u001B[39m forward_backward\u001B[38;5;241m.\u001B[39mforward()\n",
      "File \u001B[1;32mD:\\Coding Projects\\NLP-Project-Sentiment\\lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\atomic_function.py:216\u001B[0m, in \u001B[0;36mAtomicFunction.call_preflattened\u001B[1;34m(self, args)\u001B[0m\n\u001B[0;32m    214\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mcall_preflattened\u001B[39m(\u001B[38;5;28mself\u001B[39m, args: Sequence[core\u001B[38;5;241m.\u001B[39mTensor]) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m Any:\n\u001B[0;32m    215\u001B[0m \u001B[38;5;250m  \u001B[39m\u001B[38;5;124;03m\"\"\"Calls with flattened tensor inputs and returns the structured output.\"\"\"\u001B[39;00m\n\u001B[1;32m--> 216\u001B[0m   flat_outputs \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcall_flat\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    217\u001B[0m   \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mfunction_type\u001B[38;5;241m.\u001B[39mpack_output(flat_outputs)\n",
      "File \u001B[1;32mD:\\Coding Projects\\NLP-Project-Sentiment\\lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\atomic_function.py:251\u001B[0m, in \u001B[0;36mAtomicFunction.call_flat\u001B[1;34m(self, *args)\u001B[0m\n\u001B[0;32m    249\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m record\u001B[38;5;241m.\u001B[39mstop_recording():\n\u001B[0;32m    250\u001B[0m   \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_bound_context\u001B[38;5;241m.\u001B[39mexecuting_eagerly():\n\u001B[1;32m--> 251\u001B[0m     outputs \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_bound_context\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcall_function\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m    252\u001B[0m \u001B[43m        \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mname\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    253\u001B[0m \u001B[43m        \u001B[49m\u001B[38;5;28;43mlist\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43margs\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    254\u001B[0m \u001B[43m        \u001B[49m\u001B[38;5;28;43mlen\u001B[39;49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfunction_type\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mflat_outputs\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    255\u001B[0m \u001B[43m    \u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    256\u001B[0m   \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m    257\u001B[0m     outputs \u001B[38;5;241m=\u001B[39m make_call_op_in_graph(\n\u001B[0;32m    258\u001B[0m         \u001B[38;5;28mself\u001B[39m,\n\u001B[0;32m    259\u001B[0m         \u001B[38;5;28mlist\u001B[39m(args),\n\u001B[0;32m    260\u001B[0m         \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_bound_context\u001B[38;5;241m.\u001B[39mfunction_call_options\u001B[38;5;241m.\u001B[39mas_attrs(),\n\u001B[0;32m    261\u001B[0m     )\n",
      "File \u001B[1;32mD:\\Coding Projects\\NLP-Project-Sentiment\\lib\\site-packages\\tensorflow\\python\\eager\\context.py:1486\u001B[0m, in \u001B[0;36mContext.call_function\u001B[1;34m(self, name, tensor_inputs, num_outputs)\u001B[0m\n\u001B[0;32m   1484\u001B[0m cancellation_context \u001B[38;5;241m=\u001B[39m cancellation\u001B[38;5;241m.\u001B[39mcontext()\n\u001B[0;32m   1485\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m cancellation_context \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[1;32m-> 1486\u001B[0m   outputs \u001B[38;5;241m=\u001B[39m \u001B[43mexecute\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mexecute\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m   1487\u001B[0m \u001B[43m      \u001B[49m\u001B[43mname\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mdecode\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mutf-8\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   1488\u001B[0m \u001B[43m      \u001B[49m\u001B[43mnum_outputs\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mnum_outputs\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   1489\u001B[0m \u001B[43m      \u001B[49m\u001B[43minputs\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mtensor_inputs\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   1490\u001B[0m \u001B[43m      \u001B[49m\u001B[43mattrs\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mattrs\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   1491\u001B[0m \u001B[43m      \u001B[49m\u001B[43mctx\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[0;32m   1492\u001B[0m \u001B[43m  \u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m   1493\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m   1494\u001B[0m   outputs \u001B[38;5;241m=\u001B[39m execute\u001B[38;5;241m.\u001B[39mexecute_with_cancellation(\n\u001B[0;32m   1495\u001B[0m       name\u001B[38;5;241m.\u001B[39mdecode(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mutf-8\u001B[39m\u001B[38;5;124m\"\u001B[39m),\n\u001B[0;32m   1496\u001B[0m       num_outputs\u001B[38;5;241m=\u001B[39mnum_outputs,\n\u001B[1;32m   (...)\u001B[0m\n\u001B[0;32m   1500\u001B[0m       cancellation_manager\u001B[38;5;241m=\u001B[39mcancellation_context,\n\u001B[0;32m   1501\u001B[0m   )\n",
      "File \u001B[1;32mD:\\Coding Projects\\NLP-Project-Sentiment\\lib\\site-packages\\tensorflow\\python\\eager\\execute.py:53\u001B[0m, in \u001B[0;36mquick_execute\u001B[1;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001B[0m\n\u001B[0;32m     51\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m     52\u001B[0m   ctx\u001B[38;5;241m.\u001B[39mensure_initialized()\n\u001B[1;32m---> 53\u001B[0m   tensors \u001B[38;5;241m=\u001B[39m \u001B[43mpywrap_tfe\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mTFE_Py_Execute\u001B[49m\u001B[43m(\u001B[49m\u001B[43mctx\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_handle\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdevice_name\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mop_name\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m     54\u001B[0m \u001B[43m                                      \u001B[49m\u001B[43minputs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mattrs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mnum_outputs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m     55\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m core\u001B[38;5;241m.\u001B[39m_NotOkStatusException \u001B[38;5;28;01mas\u001B[39;00m e:\n\u001B[0;32m     56\u001B[0m   \u001B[38;5;28;01mif\u001B[39;00m name \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n",
      "\u001B[1;31mKeyboardInterrupt\u001B[0m: "
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Evaluation"
   ],
   "metadata": {
    "id": "nrq22GjEY80d"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "results = deepCNN.evaluate(test_inputs,test_labels,batch_size=BATCH_SIZE)\n",
    "print(results)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_FGj5NqvZAZj",
    "outputId": "7a584875-af96-4376-dea6-322625d4617e",
    "ExecuteTime": {
     "end_time": "2024-02-12T15:56:32.431159800Z",
     "start_time": "2024-02-12T15:56:31.448949500Z"
    }
   },
   "execution_count": 25,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "500/500 [==============================] - 1s 2ms/step - loss: 0.1127 - accuracy: 0.9541\n",
      "[0.11270835995674133, 0.9540625214576721]\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "# User Input"
   ],
   "metadata": {
    "id": "9SzS84ZuZ7bo"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "def processInput(text):\n",
    "    # Convert the input text to sequence\n",
    "    sequence = tokenizer.texts_to_sequences([text])\n",
    "    padded_sequence = tf.keras.preprocessing.sequence.pad_sequences(sequence, maxlen=MAX_LENGTH, padding='post', truncating='post')\n",
    "    print(padded_sequence)\n",
    "    # Predict using the sequence\n",
    "    prediction = deepCNN(np.array(padded_sequence ), training=False)\n",
    "    # Convert model's output to a user-friendly result\n",
    "    if prediction >= 0.5:\n",
    "        return f\"The result is Positive, {prediction} confidence\"\n",
    "    else:\n",
    "        return f\"The result is Negative, {1-prediction} confidence\""
   ],
   "metadata": {
    "id": "PkJAWOAnaAOb",
    "ExecuteTime": {
     "end_time": "2024-02-12T16:47:01.187094400Z",
     "start_time": "2024-02-12T16:47:01.166976600Z"
    }
   },
   "execution_count": 31,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "# This cell will be run to take user input and display the result\n",
    "\n",
    "# Take user input|\n",
    "user_text = input(\"Please enter your text: \")\n",
    "\n",
    "# Process the input and display the result\n",
    "result = processInput(user_text)\n",
    "print(result)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JtXniwEnZr2K",
    "outputId": "976fd980-9d58-4a27-e53e-485dd36a717d",
    "ExecuteTime": {
     "end_time": "2024-02-12T16:57:23.785553400Z",
     "start_time": "2024-02-12T16:57:21.533324Z"
    }
   },
   "execution_count": 34,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[146   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]]\n",
      "The result is Negative, [[0.8493748]] confidence\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "# Assume deepCNN is your model and it's correctly defined and restored from checkpoint\n",
    "model_dir = 'sentiment_analysis_model'  # Directory to save the exported model\n",
    "\n",
    "# Save the model in SavedModel format\n",
    "tf.saved_model.save(ckpt.deepCNN, model_dir)"
   ],
   "metadata": {
    "id": "2192RediqbSY",
    "ExecuteTime": {
     "end_time": "2024-02-12T15:57:01.443879200Z",
     "start_time": "2024-02-12T15:56:55.375906500Z"
    }
   },
   "execution_count": 28,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: sentiment_analysis_model\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: sentiment_analysis_model\\assets\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  }
 ]
}