imdb.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color='orange'><center>Sentiment Analysis of IMDB reviews</center></font>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color='orange'>Table of Contents:</font>                     \n",
    "    1. Importing the necessary libraries                    \n",
    "    2. Importing the dataset                    \n",
    "    3. Exploring the dataset                    \n",
    "    4. Data preprocessing                   \n",
    "    5. Train and test split                 \n",
    "    6. Creating the model                   \n",
    "    7. Training the model                   \n",
    "    8. Evaluating the model                "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color='orange'><center>1. Importing the necessary libraries</center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-03-11 19:18:11.481319: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F AVX512_VNNI FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-03-11 19:18:11.625306: I tensorflow/core/util/port.cc:104] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
      "2023-03-11 19:18:11.629162: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: :/usr/local/webots/lib/controller:/usr/local/webots/lib/webots\n",
      "2023-03-11 19:18:11.629175: I tensorflow/compiler/xla/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n",
      "2023-03-11 19:18:12.287767: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: :/usr/local/webots/lib/controller:/usr/local/webots/lib/webots\n",
      "2023-03-11 19:18:12.287873: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: :/usr/local/webots/lib/controller:/usr/local/webots/lib/webots\n",
      "2023-03-11 19:18:12.287879: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "%matplotlib inline\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import keras\n",
    "import tensorflow as tf\n",
    "from keras import layers"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color='orange'><center>3. Importing the dataset</center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.datasets import imdb\n",
    "\n",
    "(training_data, training_targets), (testing_data, testing_targets) = imdb.load_data(num_words=10000)\n",
    "\n",
    "# combining data into two separate dataframes\n",
    "data = np.concatenate((training_data, testing_data), axis=0)\n",
    "targets = np.concatenate((training_targets, testing_targets), axis=0)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color='orange'><center>3. Exploring the dataset</center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Categories: [0 1]\n",
      "Number of unique words: 9998\n"
     ]
    }
   ],
   "source": [
    "print( \"Categories:\", np.unique(targets) )\n",
    "print( \"Number of unique words:\", len(np.unique(np.hstack(data))) )\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='orange'>This shows us that we have near 10k words worth of movie reviews, and we have 0, and 1 as categories (in other words true and false)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Label has these values: [0 1]\n"
     ]
    }
   ],
   "source": [
    "print(\"The Label has these values:\", np.unique(targets))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, ..., 0, 0, 0])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "targets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:matplotlib.legend:No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7ff1f90c5f30>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=targets, palette='Set2')\n",
    "plt.legend( title = \"Is The Label Bias?\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='orange'> The data is evenly distributed, which means that the model will not be bias!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The reviews are in the following format:  [1, 194, 1153, 194, 8255, 78, 228, 5, 6, 1463, 4369, 5012, 134, 26, 4, 715, 8, 118, 1634, 14, 394, 20, 13, 119, 954, 189, 102, 5, 207, 110, 3103, 21, 14, 69, 188, 8, 30, 23, 7, 4, 249, 126, 93, 4, 114, 9, 2300, 1523, 5, 647, 4, 116, 9, 35, 8163, 4, 229, 9, 340, 1322, 4, 118, 9, 4, 130, 4901, 19, 4, 1002, 5, 89, 29, 952, 46, 37, 4, 455, 9, 45, 43, 38, 1543, 1905, 398, 4, 1649, 26, 6853, 5, 163, 11, 3215, 2, 4, 1153, 9, 194, 775, 7, 8255, 2, 349, 2637, 148, 605, 2, 8003, 15, 123, 125, 68, 2, 6853, 15, 349, 165, 4362, 98, 5, 4, 228, 9, 43, 2, 1157, 15, 299, 120, 5, 120, 174, 11, 220, 175, 136, 50, 9, 4373, 228, 8255, 5, 2, 656, 245, 2350, 5, 4, 9837, 131, 152, 491, 18, 2, 32, 7464, 1212, 14, 9, 6, 371, 78, 22, 625, 64, 1382, 9, 8, 168, 145, 23, 4, 1690, 15, 16, 4, 1355, 5, 28, 6, 52, 154, 462, 33, 89, 78, 285, 16, 145, 95]\n"
     ]
    }
   ],
   "source": [
    "print(\"The reviews are in the following format: \", data[1])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='orange'>It seems like the data is encoded. By checking the Dataset documentation, it says that the reviews can be eaxtected as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'fawn': 34701,\n",
       " 'tsukino': 52006,\n",
       " 'nunnery': 52007,\n",
       " 'sonja': 16816,\n",
       " 'vani': 63951,\n",
       " 'woods': 1408,\n",
       " 'spiders': 16115,\n",
       " 'hanging': 2345,\n",
       " 'woody': 2289,\n",
       " 'trawling': 52008,\n",
       " \"hold's\": 52009,\n",
       " 'comically': 11307,\n",
       " 'localized': 40830,\n",
       " 'disobeying': 30568,\n",
       " \"'royale\": 52010,\n",
       " \"harpo's\": 40831,\n",
       " 'canet': 52011,\n",
       " 'aileen': 19313,\n",
       " 'acurately': 52012,\n",
       " \"diplomat's\": 52013,\n",
       " 'rickman': 25242,\n",
       " 'arranged': 6746,\n",
       " 'rumbustious': 52014,\n",
       " 'familiarness': 52015,\n",
       " \"spider'\": 52016,\n",
       " 'hahahah': 68804,\n",
       " \"wood'\": 52017,\n",
       " 'transvestism': 40833,\n",
       " \"hangin'\": 34702,\n",
       " 'bringing': 2338,\n",
       " 'seamier': 40834,\n",
       " 'wooded': 34703,\n",
       " 'bravora': 52018,\n",
       " 'grueling': 16817,\n",
       " 'wooden': 1636,\n",
       " 'wednesday': 16818,\n",
       " \"'prix\": 52019,\n",
       " 'altagracia': 34704,\n",
       " 'circuitry': 52020,\n",
       " 'crotch': 11585,\n",
       " 'busybody': 57766,\n",
       " \"tart'n'tangy\": 52021,\n",
       " 'burgade': 14129,\n",
       " 'thrace': 52023,\n",
       " \"tom's\": 11038,\n",
       " 'snuggles': 52025,\n",
       " 'francesco': 29114,\n",
       " 'complainers': 52027,\n",
       " 'templarios': 52125,\n",
       " '272': 40835,\n",
       " '273': 52028,\n",
       " 'zaniacs': 52130,\n",
       " '275': 34706,\n",
       " 'consenting': 27631,\n",
       " 'snuggled': 40836,\n",
       " 'inanimate': 15492,\n",
       " 'uality': 52030,\n",
       " 'bronte': 11926,\n",
       " 'errors': 4010,\n",
       " 'dialogs': 3230,\n",
       " \"yomada's\": 52031,\n",
       " \"madman's\": 34707,\n",
       " 'dialoge': 30585,\n",
       " 'usenet': 52033,\n",
       " 'videodrome': 40837,\n",
       " \"kid'\": 26338,\n",
       " 'pawed': 52034,\n",
       " \"'girlfriend'\": 30569,\n",
       " \"'pleasure\": 52035,\n",
       " \"'reloaded'\": 52036,\n",
       " \"kazakos'\": 40839,\n",
       " 'rocque': 52037,\n",
       " 'mailings': 52038,\n",
       " 'brainwashed': 11927,\n",
       " 'mcanally': 16819,\n",
       " \"tom''\": 52039,\n",
       " 'kurupt': 25243,\n",
       " 'affiliated': 21905,\n",
       " 'babaganoosh': 52040,\n",
       " \"noe's\": 40840,\n",
       " 'quart': 40841,\n",
       " 'kids': 359,\n",
       " 'uplifting': 5034,\n",
       " 'controversy': 7093,\n",
       " 'kida': 21906,\n",
       " 'kidd': 23379,\n",
       " \"error'\": 52041,\n",
       " 'neurologist': 52042,\n",
       " 'spotty': 18510,\n",
       " 'cobblers': 30570,\n",
       " 'projection': 9878,\n",
       " 'fastforwarding': 40842,\n",
       " 'sters': 52043,\n",
       " \"eggar's\": 52044,\n",
       " 'etherything': 52045,\n",
       " 'gateshead': 40843,\n",
       " 'airball': 34708,\n",
       " 'unsinkable': 25244,\n",
       " 'stern': 7180,\n",
       " \"cervi's\": 52046,\n",
       " 'dnd': 40844,\n",
       " 'dna': 11586,\n",
       " 'insecurity': 20598,\n",
       " \"'reboot'\": 52047,\n",
       " 'trelkovsky': 11037,\n",
       " 'jaekel': 52048,\n",
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       " 'childs': 34744,\n",
       " \"portrait's\": 52277,\n",
       " 'chain': 3626,\n",
       " 'whoever': 2497,\n",
       " 'puttered': 52278,\n",
       " 'childe': 52279,\n",
       " 'maywether': 52280,\n",
       " 'chair': 3036,\n",
       " \"rance's\": 52281,\n",
       " 'machu': 34745,\n",
       " 'ballet': 4517,\n",
       " 'grapples': 34746,\n",
       " 'summerize': 76152,\n",
       " 'freelance': 30603,\n",
       " \"andrea's\": 52283,\n",
       " '\\x91very': 52284,\n",
       " 'coolidge': 45879,\n",
       " 'mache': 18518,\n",
       " 'balled': 52285,\n",
       " 'grappled': 40937,\n",
       " 'macha': 18519,\n",
       " 'underlining': 21921,\n",
       " 'macho': 5623,\n",
       " 'oversight': 19507,\n",
       " 'machi': 25257,\n",
       " 'verbally': 11311,\n",
       " 'tenacious': 21922,\n",
       " 'windshields': 40938,\n",
       " 'paychecks': 18557,\n",
       " 'jerk': 3396,\n",
       " \"good'\": 11931,\n",
       " 'prancer': 34748,\n",
       " 'prances': 21923,\n",
       " 'olympus': 52286,\n",
       " 'lark': 21924,\n",
       " 'embark': 10785,\n",
       " 'gloomy': 7365,\n",
       " 'jehaan': 52287,\n",
       " 'turaqui': 52288,\n",
       " \"child'\": 20607,\n",
       " 'locked': 2894,\n",
       " 'pranced': 52289,\n",
       " 'exact': 2588,\n",
       " 'unattuned': 52290,\n",
       " 'minute': 783,\n",
       " 'skewed': 16118,\n",
       " 'hodgins': 40940,\n",
       " 'skewer': 34749,\n",
       " 'think\\x85': 52291,\n",
       " 'rosenstein': 38765,\n",
       " 'helmit': 52292,\n",
       " 'wrestlemanias': 34750,\n",
       " 'hindered': 16826,\n",
       " \"martha's\": 30604,\n",
       " 'cheree': 52293,\n",
       " \"pluckin'\": 52294,\n",
       " 'ogles': 40941,\n",
       " 'heavyweight': 11932,\n",
       " 'aada': 82190,\n",
       " 'chopping': 11312,\n",
       " 'strongboy': 61534,\n",
       " 'hegemonic': 41342,\n",
       " 'adorns': 40942,\n",
       " 'xxth': 41346,\n",
       " 'nobuhiro': 34751,\n",
       " 'capitães': 52298,\n",
       " 'kavogianni': 52299,\n",
       " 'antwerp': 13422,\n",
       " 'celebrated': 6538,\n",
       " 'roarke': 52300,\n",
       " 'baggins': 40943,\n",
       " 'cheeseburgers': 31270,\n",
       " 'matras': 52301,\n",
       " \"nineties'\": 52302,\n",
       " \"'craig'\": 52303,\n",
       " 'celebrates': 12999,\n",
       " 'unintentionally': 3383,\n",
       " 'drafted': 14362,\n",
       " 'climby': 52304,\n",
       " '303': 52305,\n",
       " 'oldies': 18520,\n",
       " 'climbs': 9096,\n",
       " 'honour': 9655,\n",
       " 'plucking': 34752,\n",
       " '305': 30074,\n",
       " 'address': 5514,\n",
       " 'menjou': 40944,\n",
       " \"'freak'\": 42592,\n",
       " 'dwindling': 19508,\n",
       " 'benson': 9458,\n",
       " 'white’s': 52307,\n",
       " 'shamelessness': 40945,\n",
       " 'impacted': 21925,\n",
       " 'upatz': 52308,\n",
       " 'cusack': 3840,\n",
       " \"flavia's\": 37567,\n",
       " 'effette': 52309,\n",
       " 'influx': 34753,\n",
       " 'boooooooo': 52310,\n",
       " 'dimitrova': 52311,\n",
       " 'houseman': 13423,\n",
       " 'bigas': 25259,\n",
       " 'boylen': 52312,\n",
       " 'phillipenes': 52313,\n",
       " 'fakery': 40946,\n",
       " \"grandpa's\": 27658,\n",
       " 'darnell': 27659,\n",
       " 'undergone': 19509,\n",
       " 'handbags': 52315,\n",
       " 'perished': 21926,\n",
       " 'pooped': 37778,\n",
       " 'vigour': 27660,\n",
       " 'opposed': 3627,\n",
       " 'etude': 52316,\n",
       " \"caine's\": 11799,\n",
       " 'doozers': 52317,\n",
       " 'photojournals': 34754,\n",
       " 'perishes': 52318,\n",
       " 'constrains': 34755,\n",
       " 'migenes': 40948,\n",
       " 'consoled': 30605,\n",
       " 'alastair': 16827,\n",
       " 'wvs': 52319,\n",
       " 'ooooooh': 52320,\n",
       " 'approving': 34756,\n",
       " 'consoles': 40949,\n",
       " 'disparagement': 52064,\n",
       " 'futureistic': 52322,\n",
       " 'rebounding': 52323,\n",
       " \"'date\": 52324,\n",
       " 'gregoire': 52325,\n",
       " 'rutherford': 21927,\n",
       " 'americanised': 34757,\n",
       " 'novikov': 82196,\n",
       " 'following': 1042,\n",
       " 'munroe': 34758,\n",
       " \"morita'\": 52326,\n",
       " 'christenssen': 52327,\n",
       " 'oatmeal': 23106,\n",
       " 'fossey': 25260,\n",
       " 'livered': 40950,\n",
       " 'listens': 13000,\n",
       " \"'marci\": 76164,\n",
       " \"otis's\": 52330,\n",
       " 'thanking': 23387,\n",
       " 'maude': 16019,\n",
       " 'extensions': 34759,\n",
       " 'ameteurish': 52332,\n",
       " \"commender's\": 52333,\n",
       " 'agricultural': 27661,\n",
       " 'convincingly': 4518,\n",
       " 'fueled': 17639,\n",
       " 'mahattan': 54014,\n",
       " \"paris's\": 40952,\n",
       " 'vulkan': 52336,\n",
       " 'stapes': 52337,\n",
       " 'odysessy': 52338,\n",
       " 'harmon': 12259,\n",
       " 'surfing': 4252,\n",
       " 'halloran': 23494,\n",
       " 'unbelieveably': 49580,\n",
       " \"'offed'\": 52339,\n",
       " 'quadrant': 30607,\n",
       " 'inhabiting': 19510,\n",
       " 'nebbish': 34760,\n",
       " 'forebears': 40953,\n",
       " 'skirmish': 34761,\n",
       " 'ocassionally': 52340,\n",
       " \"'resist\": 52341,\n",
       " 'impactful': 21928,\n",
       " 'spicier': 52342,\n",
       " 'touristy': 40954,\n",
       " \"'football'\": 52343,\n",
       " 'webpage': 40955,\n",
       " 'exurbia': 52345,\n",
       " 'jucier': 52346,\n",
       " 'professors': 14901,\n",
       " 'structuring': 34762,\n",
       " 'jig': 30608,\n",
       " 'overlord': 40956,\n",
       " 'disconnect': 25261,\n",
       " 'sniffle': 82201,\n",
       " 'slimeball': 40957,\n",
       " 'jia': 40958,\n",
       " 'milked': 16828,\n",
       " 'banjoes': 40959,\n",
       " 'jim': 1237,\n",
       " 'workforces': 52348,\n",
       " 'jip': 52349,\n",
       " 'rotweiller': 52350,\n",
       " 'mundaneness': 34763,\n",
       " \"'ninja'\": 52351,\n",
       " \"dead'\": 11040,\n",
       " \"cipriani's\": 40960,\n",
       " 'modestly': 20608,\n",
       " \"professor'\": 52352,\n",
       " 'shacked': 40961,\n",
       " 'bashful': 34764,\n",
       " 'sorter': 23388,\n",
       " 'overpowering': 16120,\n",
       " 'workmanlike': 18521,\n",
       " 'henpecked': 27662,\n",
       " 'sorted': 18522,\n",
       " \"jōb's\": 52354,\n",
       " \"'always\": 52355,\n",
       " \"'baptists\": 34765,\n",
       " 'dreamcatchers': 52356,\n",
       " \"'silence'\": 52357,\n",
       " 'hickory': 21929,\n",
       " 'fun\\x97yet': 52358,\n",
       " 'breakumentary': 52359,\n",
       " 'didn': 15496,\n",
       " 'didi': 52360,\n",
       " 'pealing': 52361,\n",
       " 'dispite': 40962,\n",
       " \"italy's\": 25262,\n",
       " 'instability': 21930,\n",
       " 'quarter': 6539,\n",
       " 'quartet': 12608,\n",
       " 'padmé': 52362,\n",
       " \"'bleedmedry\": 52363,\n",
       " 'pahalniuk': 52364,\n",
       " 'honduras': 52365,\n",
       " 'bursting': 10786,\n",
       " \"pablo's\": 41465,\n",
       " 'irremediably': 52367,\n",
       " 'presages': 40963,\n",
       " 'bowlegged': 57832,\n",
       " 'dalip': 65183,\n",
       " 'entering': 6260,\n",
       " 'newsradio': 76172,\n",
       " 'presaged': 54150,\n",
       " \"giallo's\": 27663,\n",
       " 'bouyant': 40964,\n",
       " 'amerterish': 52368,\n",
       " 'rajni': 18523,\n",
       " 'leeves': 30610,\n",
       " 'macauley': 34767,\n",
       " 'seriously': 612,\n",
       " 'sugercoma': 52369,\n",
       " 'grimstead': 52370,\n",
       " \"'fairy'\": 52371,\n",
       " 'zenda': 30611,\n",
       " \"'twins'\": 52372,\n",
       " 'realisation': 17640,\n",
       " 'highsmith': 27664,\n",
       " 'raunchy': 7817,\n",
       " 'incentives': 40965,\n",
       " 'flatson': 52374,\n",
       " 'snooker': 35097,\n",
       " 'crazies': 16829,\n",
       " 'crazier': 14902,\n",
       " 'grandma': 7094,\n",
       " 'napunsaktha': 52375,\n",
       " 'workmanship': 30612,\n",
       " 'reisner': 52376,\n",
       " \"sanford's\": 61306,\n",
       " '\\x91doña': 52377,\n",
       " 'modest': 6108,\n",
       " \"everything's\": 19153,\n",
       " 'hamer': 40966,\n",
       " \"couldn't'\": 52379,\n",
       " 'quibble': 13001,\n",
       " 'socking': 52380,\n",
       " 'tingler': 21931,\n",
       " 'gutman': 52381,\n",
       " 'lachlan': 40967,\n",
       " 'tableaus': 52382,\n",
       " 'headbanger': 52383,\n",
       " 'spoken': 2847,\n",
       " 'cerebrally': 34768,\n",
       " \"'road\": 23490,\n",
       " 'tableaux': 21932,\n",
       " \"proust's\": 40968,\n",
       " 'periodical': 40969,\n",
       " \"shoveller's\": 52385,\n",
       " 'tamara': 25263,\n",
       " 'affords': 17641,\n",
       " 'concert': 3249,\n",
       " \"yara's\": 87955,\n",
       " 'someome': 52386,\n",
       " 'lingering': 8424,\n",
       " \"abraham's\": 41511,\n",
       " 'beesley': 34769,\n",
       " 'cherbourg': 34770,\n",
       " 'kagan': 28624,\n",
       " 'snatch': 9097,\n",
       " \"miyazaki's\": 9260,\n",
       " 'absorbs': 25264,\n",
       " \"koltai's\": 40970,\n",
       " 'tingled': 64027,\n",
       " 'crossroads': 19511,\n",
       " 'rehab': 16121,\n",
       " 'falworth': 52389,\n",
       " 'sequals': 52390,\n",
       " ...}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_index = imdb.get_word_index()\n",
    "word_index"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color='orange'><center>4. Data preprocessing</center>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='orange'>The documentation says that this line will contain the indecies of the words used in the reviews, we only need to map them, in order to read the reviews!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert(sequences):\n",
    "    results = np.zeros((len(sequences), 10000))\n",
    "    for i, sequence in enumerate(sequences):\n",
    "        results[i, sequence] = 1\n",
    "    return results\n",
    " \n",
    "data = convert(data)\n",
    "targets = np.array(targets).astype(\"float32\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color='orange'><center>5. Train and test split</center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting test data to be the first 10_000 items of the dataframe\n",
    "X_test = data[:10000]\n",
    "y_test = targets[:10000]\n",
    "\n",
    "# Splitting test data to be the last 10_000 items of the dataframe\n",
    "X_train = data[10000:]\n",
    "y_train = targets[10000:]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color='orange'><center>6. Creating the model</center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense (Dense)               (None, 32)                320032    \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 32)                0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 64)                2112      \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 64)                0         \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 64)                4160      \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 1)                 65        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 326,369\n",
      "Trainable params: 326,369\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-03-11 19:18:19.417778: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: :/usr/local/webots/lib/controller:/usr/local/webots/lib/webots\n",
      "2023-03-11 19:18:19.417805: W tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:265] failed call to cuInit: UNKNOWN ERROR (303)\n",
      "2023-03-11 19:18:19.417828: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (Johnny): /proc/driver/nvidia/version does not exist\n",
      "2023-03-11 19:18:19.418125: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F AVX512_VNNI FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "source": [
    "model = keras.Sequential()\n",
    "model.add( layers.Dense(32, activation = tf.keras.activations.relu, input_shape=(10000, )) )\n",
    "model.add( layers.Dropout(0.3) )\n",
    "model.add( layers.Dense(64, activation = tf.keras.activations.gelu) )\n",
    "model.add( layers.Dropout(0.2) )\n",
    "model.add( layers.Dense(64, activation = tf.keras.activations.selu) )\n",
    "model.add( layers.Dense(1, activation = tf.keras.activations.sigmoid) )\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile( optimizer = \"adam\",\n",
    "               loss = \"binary_crossentropy\",\n",
    "               metrics = [\"accuracy\"] )"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color='orange'><center>7. Training the model</center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/3\n",
      "40/40 [==============================] - 2s 38ms/step - loss: 0.4378 - accuracy: 0.8041 - val_loss: 0.2722 - val_accuracy: 0.8907\n",
      "Epoch 2/3\n",
      "40/40 [==============================] - 1s 25ms/step - loss: 0.2279 - accuracy: 0.9111 - val_loss: 0.2610 - val_accuracy: 0.8964\n",
      "Epoch 3/3\n",
      "40/40 [==============================] - 1s 21ms/step - loss: 0.1695 - accuracy: 0.9365 - val_loss: 0.2791 - val_accuracy: 0.8932\n"
     ]
    }
   ],
   "source": [
    "results = model.fit( X_train, y_train, epochs= 3, batch_size = 1024, validation_data = (X_test, y_test) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color='orange'><center>8. Evaluating the model</center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average accuarcy of the model is:  89.34\n",
      "The average loss of the modeli is:  27.08\n"
     ]
    }
   ],
   "source": [
    "print(\"The average accuarcy of the model is: \", np.mean(results.history[\"val_accuracy\"]).round(4) * 100)\n",
    "print(\"The average loss of the modeli is: \", np.mean(results.history[\"val_loss\"]).round(4) * 100)"
   ]
  }
 ],
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