Imdb_movie_review.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# IMDB movie review"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We all have at one time or the other have checked the IMDB rating of certain films in the internet. Movie fanatics like me always seem to rate movies based on IMDB ratings since I feel its quite transparent. So, have you wondered how the ratings are made? \n",
    "\n",
    "Well we are going to uncover that here only.\n",
    "\n",
    "Basically IMDB movie reviews are Deep Learning tasks that take into account the movie reviews and gives their ratings based on that.\n",
    "\n",
    "In this notebook we will use the IMDB dataset, prebuilt in Keras to make our own movie review system. But, we won't go much deeper and use Dropout and all, instead we are going to keep it simple and just place the  basics in.\n",
    "\n",
    "So lets dive in by importing the necessary packages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Don't get frightened by seeing imports that was very hard to guess in the very first cell only, but I imported them.\n",
    "If you are wondering how I did them, well, I have this habit of coming back and importing the necessary packages in the first cell only."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing the necessary packages\n",
    "import tensorflow as tf\n",
    "\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "\n",
    "from tensorflow.keras.datasets import imdb\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we are done with loading our packages, lets dive into loading our dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1: Loading the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#loading the dataset and taking the top 10,000 most used words for the word dictionary\n",
    "(review_train , label_train) , (review_test , label_test) = imdb.load_data(num_words = 10000) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You might be wondering how did I know to put num_words = 10,000 to cut the dictionary and get the top 10,000 most used words only.\n",
    "\n",
    "Well, I just looked up in the Keras Documentation from the following link:\n",
    "https://keras.io/api/datasets/imdb/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The training data of features are basically a collection of the indexes. \n",
    "To check the words, keras presents a get_word_index() function that returns a word_index dictionary where the keys are the words and the values are their indexes.\n",
    "\n",
    "Now to check what sentence were written we need to reverse the dictionary and then find the words."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Analyzing the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = np.asarray(label_train).astype('float32')\n",
    "y_test = np.asarray(label_test).astype('float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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",
       " 'sidebars': 52049,\n",
       " \"sforza's\": 52050,\n",
       " 'distortions': 17633,\n",
       " 'mutinies': 52051,\n",
       " 'sermons': 30602,\n",
       " '7ft': 40846,\n",
       " 'boobage': 52052,\n",
       " \"o'bannon's\": 52053,\n",
       " 'populations': 23380,\n",
       " 'chulak': 52054,\n",
       " 'mesmerize': 27633,\n",
       " 'quinnell': 52055,\n",
       " 'yahoo': 10307,\n",
       " 'meteorologist': 52057,\n",
       " 'beswick': 42577,\n",
       " 'boorman': 15493,\n",
       " 'voicework': 40847,\n",
       " \"ster'\": 52058,\n",
       " 'blustering': 22922,\n",
       " 'hj': 52059,\n",
       " 'intake': 27634,\n",
       " 'morally': 5621,\n",
       " 'jumbling': 40849,\n",
       " 'bowersock': 52060,\n",
       " \"'porky's'\": 52061,\n",
       " 'gershon': 16821,\n",
       " 'ludicrosity': 40850,\n",
       " 'coprophilia': 52062,\n",
       " 'expressively': 40851,\n",
       " \"india's\": 19500,\n",
       " \"post's\": 34710,\n",
       " 'wana': 52063,\n",
       " 'wang': 5283,\n",
       " 'wand': 30571,\n",
       " 'wane': 25245,\n",
       " 'edgeways': 52321,\n",
       " 'titanium': 34711,\n",
       " 'pinta': 40852,\n",
       " 'want': 178,\n",
       " 'pinto': 30572,\n",
       " 'whoopdedoodles': 52065,\n",
       " 'tchaikovsky': 21908,\n",
       " 'travel': 2103,\n",
       " \"'victory'\": 52066,\n",
       " 'copious': 11928,\n",
       " 'gouge': 22433,\n",
       " \"chapters'\": 52067,\n",
       " 'barbra': 6702,\n",
       " 'uselessness': 30573,\n",
       " \"wan'\": 52068,\n",
       " 'assimilated': 27635,\n",
       " 'petiot': 16116,\n",
       " 'most\\x85and': 52069,\n",
       " 'dinosaurs': 3930,\n",
       " 'wrong': 352,\n",
       " 'seda': 52070,\n",
       " 'stollen': 52071,\n",
       " 'sentencing': 34712,\n",
       " 'ouroboros': 40853,\n",
       " 'assimilates': 40854,\n",
       " 'colorfully': 40855,\n",
       " 'glenne': 27636,\n",
       " 'dongen': 52072,\n",
       " 'subplots': 4760,\n",
       " 'kiloton': 52073,\n",
       " 'chandon': 23381,\n",
       " \"effect'\": 34713,\n",
       " 'snugly': 27637,\n",
       " 'kuei': 40856,\n",
       " 'welcomed': 9092,\n",
       " 'dishonor': 30071,\n",
       " 'concurrence': 52075,\n",
       " 'stoicism': 23382,\n",
       " \"guys'\": 14896,\n",
       " \"beroemd'\": 52077,\n",
       " 'butcher': 6703,\n",
       " \"melfi's\": 40857,\n",
       " 'aargh': 30623,\n",
       " 'playhouse': 20599,\n",
       " 'wickedly': 11308,\n",
       " 'fit': 1180,\n",
       " 'labratory': 52078,\n",
       " 'lifeline': 40859,\n",
       " 'screaming': 1927,\n",
       " 'fix': 4287,\n",
       " 'cineliterate': 52079,\n",
       " 'fic': 52080,\n",
       " 'fia': 52081,\n",
       " 'fig': 34714,\n",
       " 'fmvs': 52082,\n",
       " 'fie': 52083,\n",
       " 'reentered': 52084,\n",
       " 'fin': 30574,\n",
       " 'doctresses': 52085,\n",
       " 'fil': 52086,\n",
       " 'zucker': 12606,\n",
       " 'ached': 31931,\n",
       " 'counsil': 52088,\n",
       " 'paterfamilias': 52089,\n",
       " 'songwriter': 13885,\n",
       " 'shivam': 34715,\n",
       " 'hurting': 9654,\n",
       " 'effects': 299,\n",
       " 'slauther': 52090,\n",
       " \"'flame'\": 52091,\n",
       " 'sommerset': 52092,\n",
       " 'interwhined': 52093,\n",
       " 'whacking': 27638,\n",
       " 'bartok': 52094,\n",
       " 'barton': 8775,\n",
       " 'frewer': 21909,\n",
       " \"fi'\": 52095,\n",
       " 'ingrid': 6192,\n",
       " 'stribor': 30575,\n",
       " 'approporiately': 52096,\n",
       " 'wobblyhand': 52097,\n",
       " 'tantalisingly': 52098,\n",
       " 'ankylosaurus': 52099,\n",
       " 'parasites': 17634,\n",
       " 'childen': 52100,\n",
       " \"jenkins'\": 52101,\n",
       " 'metafiction': 52102,\n",
       " 'golem': 17635,\n",
       " 'indiscretion': 40860,\n",
       " \"reeves'\": 23383,\n",
       " \"inamorata's\": 57781,\n",
       " 'brittannica': 52104,\n",
       " 'adapt': 7916,\n",
       " \"russo's\": 30576,\n",
       " 'guitarists': 48246,\n",
       " 'abbott': 10553,\n",
       " 'abbots': 40861,\n",
       " 'lanisha': 17649,\n",
       " 'magickal': 40863,\n",
       " 'mattter': 52105,\n",
       " \"'willy\": 52106,\n",
       " 'pumpkins': 34716,\n",
       " 'stuntpeople': 52107,\n",
       " 'estimate': 30577,\n",
       " 'ugghhh': 40864,\n",
       " 'gameplay': 11309,\n",
       " \"wern't\": 52108,\n",
       " \"n'sync\": 40865,\n",
       " 'sickeningly': 16117,\n",
       " 'chiara': 40866,\n",
       " 'disturbed': 4011,\n",
       " 'portmanteau': 40867,\n",
       " 'ineffectively': 52109,\n",
       " \"duchonvey's\": 82143,\n",
       " \"nasty'\": 37519,\n",
       " 'purpose': 1285,\n",
       " 'lazers': 52112,\n",
       " 'lightened': 28105,\n",
       " 'kaliganj': 52113,\n",
       " 'popularism': 52114,\n",
       " \"damme's\": 18511,\n",
       " 'stylistics': 30578,\n",
       " 'mindgaming': 52115,\n",
       " 'spoilerish': 46449,\n",
       " \"'corny'\": 52117,\n",
       " 'boerner': 34718,\n",
       " 'olds': 6792,\n",
       " 'bakelite': 52118,\n",
       " 'renovated': 27639,\n",
       " 'forrester': 27640,\n",
       " \"lumiere's\": 52119,\n",
       " 'gaskets': 52024,\n",
       " 'needed': 884,\n",
       " 'smight': 34719,\n",
       " 'master': 1297,\n",
       " \"edie's\": 25905,\n",
       " 'seeber': 40868,\n",
       " 'hiya': 52120,\n",
       " 'fuzziness': 52121,\n",
       " 'genesis': 14897,\n",
       " 'rewards': 12607,\n",
       " 'enthrall': 30579,\n",
       " \"'about\": 40869,\n",
       " \"recollection's\": 52122,\n",
       " 'mutilated': 11039,\n",
       " 'fatherlands': 52123,\n",
       " \"fischer's\": 52124,\n",
       " 'positively': 5399,\n",
       " '270': 34705,\n",
       " 'ahmed': 34720,\n",
       " 'zatoichi': 9836,\n",
       " 'bannister': 13886,\n",
       " 'anniversaries': 52127,\n",
       " \"helm's\": 30580,\n",
       " \"'work'\": 52128,\n",
       " 'exclaimed': 34721,\n",
       " \"'unfunny'\": 52129,\n",
       " '274': 52029,\n",
       " 'feeling': 544,\n",
       " \"wanda's\": 52131,\n",
       " 'dolan': 33266,\n",
       " '278': 52133,\n",
       " 'peacoat': 52134,\n",
       " 'brawny': 40870,\n",
       " 'mishra': 40871,\n",
       " 'worlders': 40872,\n",
       " 'protags': 52135,\n",
       " 'skullcap': 52136,\n",
       " 'dastagir': 57596,\n",
       " 'affairs': 5622,\n",
       " 'wholesome': 7799,\n",
       " 'hymen': 52137,\n",
       " 'paramedics': 25246,\n",
       " 'unpersons': 52138,\n",
       " 'heavyarms': 52139,\n",
       " 'affaire': 52140,\n",
       " 'coulisses': 52141,\n",
       " 'hymer': 40873,\n",
       " 'kremlin': 52142,\n",
       " 'shipments': 30581,\n",
       " 'pixilated': 52143,\n",
       " \"'00s\": 30582,\n",
       " 'diminishing': 18512,\n",
       " 'cinematic': 1357,\n",
       " 'resonates': 14898,\n",
       " 'simplify': 40874,\n",
       " \"nature'\": 40875,\n",
       " 'temptresses': 40876,\n",
       " 'reverence': 16822,\n",
       " 'resonated': 19502,\n",
       " 'dailey': 34722,\n",
       " '2\\x85': 52144,\n",
       " 'treize': 27641,\n",
       " 'majo': 52145,\n",
       " 'kiya': 21910,\n",
       " 'woolnough': 52146,\n",
       " 'thanatos': 39797,\n",
       " 'sandoval': 35731,\n",
       " 'dorama': 40879,\n",
       " \"o'shaughnessy\": 52147,\n",
       " 'tech': 4988,\n",
       " 'fugitives': 32018,\n",
       " 'teck': 30583,\n",
       " \"'e'\": 76125,\n",
       " 'doesn’t': 40881,\n",
       " 'purged': 52149,\n",
       " 'saying': 657,\n",
       " \"martians'\": 41095,\n",
       " 'norliss': 23418,\n",
       " 'dickey': 27642,\n",
       " 'dicker': 52152,\n",
       " \"'sependipity\": 52153,\n",
       " 'padded': 8422,\n",
       " 'ordell': 57792,\n",
       " \"sturges'\": 40882,\n",
       " 'independentcritics': 52154,\n",
       " 'tempted': 5745,\n",
       " \"atkinson's\": 34724,\n",
       " 'hounded': 25247,\n",
       " 'apace': 52155,\n",
       " 'clicked': 15494,\n",
       " \"'humor'\": 30584,\n",
       " \"martino's\": 17177,\n",
       " \"'supporting\": 52156,\n",
       " 'warmongering': 52032,\n",
       " \"zemeckis's\": 34725,\n",
       " 'lube': 21911,\n",
       " 'shocky': 52157,\n",
       " 'plate': 7476,\n",
       " 'plata': 40883,\n",
       " 'sturgess': 40884,\n",
       " \"nerds'\": 40885,\n",
       " 'plato': 20600,\n",
       " 'plath': 34726,\n",
       " 'platt': 40886,\n",
       " 'mcnab': 52159,\n",
       " 'clumsiness': 27643,\n",
       " 'altogether': 3899,\n",
       " 'massacring': 42584,\n",
       " 'bicenntinial': 52160,\n",
       " 'skaal': 40887,\n",
       " 'droning': 14360,\n",
       " 'lds': 8776,\n",
       " 'jaguar': 21912,\n",
       " \"cale's\": 34727,\n",
       " 'nicely': 1777,\n",
       " 'mummy': 4588,\n",
       " \"lot's\": 18513,\n",
       " 'patch': 10086,\n",
       " 'kerkhof': 50202,\n",
       " \"leader's\": 52161,\n",
       " \"'movie\": 27644,\n",
       " 'uncomfirmed': 52162,\n",
       " 'heirloom': 40888,\n",
       " 'wrangle': 47360,\n",
       " 'emotion\\x85': 52163,\n",
       " \"'stargate'\": 52164,\n",
       " 'pinoy': 40889,\n",
       " 'conchatta': 40890,\n",
       " 'broeke': 41128,\n",
       " 'advisedly': 40891,\n",
       " \"barker's\": 17636,\n",
       " 'descours': 52166,\n",
       " 'lots': 772,\n",
       " 'lotr': 9259,\n",
       " 'irs': 9879,\n",
       " 'lott': 52167,\n",
       " 'xvi': 40892,\n",
       " 'irk': 34728,\n",
       " 'irl': 52168,\n",
       " 'ira': 6887,\n",
       " 'belzer': 21913,\n",
       " 'irc': 52169,\n",
       " 'ire': 27645,\n",
       " 'requisites': 40893,\n",
       " 'discipline': 7693,\n",
       " 'lyoko': 52961,\n",
       " 'extend': 11310,\n",
       " 'nature': 873,\n",
       " \"'dickie'\": 52170,\n",
       " 'optimist': 40894,\n",
       " 'lapping': 30586,\n",
       " 'superficial': 3900,\n",
       " 'vestment': 52171,\n",
       " 'extent': 2823,\n",
       " 'tendons': 52172,\n",
       " \"heller's\": 52173,\n",
       " 'quagmires': 52174,\n",
       " 'miyako': 52175,\n",
       " 'moocow': 20601,\n",
       " \"coles'\": 52176,\n",
       " 'lookit': 40895,\n",
       " 'ravenously': 52177,\n",
       " 'levitating': 40896,\n",
       " 'perfunctorily': 52178,\n",
       " 'lookin': 30587,\n",
       " \"lot'\": 40898,\n",
       " 'lookie': 52179,\n",
       " 'fearlessly': 34870,\n",
       " 'libyan': 52181,\n",
       " 'fondles': 40899,\n",
       " 'gopher': 35714,\n",
       " 'wearying': 40901,\n",
       " \"nz's\": 52182,\n",
       " 'minuses': 27646,\n",
       " 'puposelessly': 52183,\n",
       " 'shandling': 52184,\n",
       " 'decapitates': 31268,\n",
       " 'humming': 11929,\n",
       " \"'nother\": 40902,\n",
       " 'smackdown': 21914,\n",
       " 'underdone': 30588,\n",
       " 'frf': 40903,\n",
       " 'triviality': 52185,\n",
       " 'fro': 25248,\n",
       " 'bothers': 8777,\n",
       " \"'kensington\": 52186,\n",
       " 'much': 73,\n",
       " 'muco': 34730,\n",
       " 'wiseguy': 22615,\n",
       " \"richie's\": 27648,\n",
       " 'tonino': 40904,\n",
       " 'unleavened': 52187,\n",
       " 'fry': 11587,\n",
       " \"'tv'\": 40905,\n",
       " 'toning': 40906,\n",
       " 'obese': 14361,\n",
       " 'sensationalized': 30589,\n",
       " 'spiv': 40907,\n",
       " 'spit': 6259,\n",
       " 'arkin': 7364,\n",
       " 'charleton': 21915,\n",
       " 'jeon': 16823,\n",
       " 'boardroom': 21916,\n",
       " 'doubts': 4989,\n",
       " 'spin': 3084,\n",
       " 'hepo': 53083,\n",
       " 'wildcat': 27649,\n",
       " 'venoms': 10584,\n",
       " 'misconstrues': 52191,\n",
       " 'mesmerising': 18514,\n",
       " 'misconstrued': 40908,\n",
       " 'rescinds': 52192,\n",
       " 'prostrate': 52193,\n",
       " 'majid': 40909,\n",
       " 'climbed': 16479,\n",
       " 'canoeing': 34731,\n",
       " 'majin': 52195,\n",
       " 'animie': 57804,\n",
       " 'sylke': 40910,\n",
       " 'conditioned': 14899,\n",
       " 'waddell': 40911,\n",
       " '3\\x85': 52196,\n",
       " 'hyperdrive': 41188,\n",
       " 'conditioner': 34732,\n",
       " 'bricklayer': 53153,\n",
       " 'hong': 2576,\n",
       " 'memoriam': 52198,\n",
       " 'inventively': 30592,\n",
       " \"levant's\": 25249,\n",
       " 'portobello': 20638,\n",
       " 'remand': 52200,\n",
       " 'mummified': 19504,\n",
       " 'honk': 27650,\n",
       " 'spews': 19505,\n",
       " 'visitations': 40912,\n",
       " 'mummifies': 52201,\n",
       " 'cavanaugh': 25250,\n",
       " 'zeon': 23385,\n",
       " \"jungle's\": 40913,\n",
       " 'viertel': 34733,\n",
       " 'frenchmen': 27651,\n",
       " 'torpedoes': 52202,\n",
       " 'schlessinger': 52203,\n",
       " 'torpedoed': 34734,\n",
       " 'blister': 69876,\n",
       " 'cinefest': 52204,\n",
       " 'furlough': 34735,\n",
       " 'mainsequence': 52205,\n",
       " 'mentors': 40914,\n",
       " 'academic': 9094,\n",
       " 'stillness': 20602,\n",
       " 'academia': 40915,\n",
       " 'lonelier': 52206,\n",
       " 'nibby': 52207,\n",
       " \"losers'\": 52208,\n",
       " 'cineastes': 40916,\n",
       " 'corporate': 4449,\n",
       " 'massaging': 40917,\n",
       " 'bellow': 30593,\n",
       " 'absurdities': 19506,\n",
       " 'expetations': 53241,\n",
       " 'nyfiken': 40918,\n",
       " 'mehras': 75638,\n",
       " 'lasse': 52209,\n",
       " 'visability': 52210,\n",
       " 'militarily': 33946,\n",
       " \"elder'\": 52211,\n",
       " 'gainsbourg': 19023,\n",
       " 'hah': 20603,\n",
       " 'hai': 13420,\n",
       " 'haj': 34736,\n",
       " 'hak': 25251,\n",
       " 'hal': 4311,\n",
       " 'ham': 4892,\n",
       " 'duffer': 53259,\n",
       " 'haa': 52213,\n",
       " 'had': 66,\n",
       " 'advancement': 11930,\n",
       " 'hag': 16825,\n",
       " \"hand'\": 25252,\n",
       " 'hay': 13421,\n",
       " 'mcnamara': 20604,\n",
       " \"mozart's\": 52214,\n",
       " 'duffel': 30731,\n",
       " 'haq': 30594,\n",
       " 'har': 13887,\n",
       " 'has': 44,\n",
       " 'hat': 2401,\n",
       " 'hav': 40919,\n",
       " 'haw': 30595,\n",
       " 'figtings': 52215,\n",
       " 'elders': 15495,\n",
       " 'underpanted': 52216,\n",
       " 'pninson': 52217,\n",
       " 'unequivocally': 27652,\n",
       " \"barbara's\": 23673,\n",
       " \"bello'\": 52219,\n",
       " 'indicative': 12997,\n",
       " 'yawnfest': 40920,\n",
       " 'hexploitation': 52220,\n",
       " \"loder's\": 52221,\n",
       " 'sleuthing': 27653,\n",
       " \"justin's\": 32622,\n",
       " \"'ball\": 52222,\n",
       " \"'summer\": 52223,\n",
       " \"'demons'\": 34935,\n",
       " \"mormon's\": 52225,\n",
       " \"laughton's\": 34737,\n",
       " 'debell': 52226,\n",
       " 'shipyard': 39724,\n",
       " 'unabashedly': 30597,\n",
       " 'disks': 40401,\n",
       " 'crowd': 2290,\n",
       " 'crowe': 10087,\n",
       " \"vancouver's\": 56434,\n",
       " 'mosques': 34738,\n",
       " 'crown': 6627,\n",
       " 'culpas': 52227,\n",
       " 'crows': 27654,\n",
       " 'surrell': 53344,\n",
       " 'flowless': 52229,\n",
       " 'sheirk': 52230,\n",
       " \"'three\": 40923,\n",
       " \"peterson'\": 52231,\n",
       " 'ooverall': 52232,\n",
       " 'perchance': 40924,\n",
       " 'bottom': 1321,\n",
       " 'chabert': 53363,\n",
       " 'sneha': 52233,\n",
       " 'inhuman': 13888,\n",
       " 'ichii': 52234,\n",
       " 'ursla': 52235,\n",
       " 'completly': 30598,\n",
       " 'moviedom': 40925,\n",
       " 'raddick': 52236,\n",
       " 'brundage': 51995,\n",
       " 'brigades': 40926,\n",
       " 'starring': 1181,\n",
       " \"'goal'\": 52237,\n",
       " 'caskets': 52238,\n",
       " 'willcock': 52239,\n",
       " \"threesome's\": 52240,\n",
       " \"mosque'\": 52241,\n",
       " \"cover's\": 52242,\n",
       " 'spaceships': 17637,\n",
       " 'anomalous': 40927,\n",
       " 'ptsd': 27655,\n",
       " 'shirdan': 52243,\n",
       " 'obscenity': 21962,\n",
       " 'lemmings': 30599,\n",
       " 'duccio': 30600,\n",
       " \"levene's\": 52244,\n",
       " \"'gorby'\": 52245,\n",
       " \"teenager's\": 25255,\n",
       " 'marshall': 5340,\n",
       " 'honeymoon': 9095,\n",
       " 'shoots': 3231,\n",
       " 'despised': 12258,\n",
       " 'okabasho': 52246,\n",
       " 'fabric': 8289,\n",
       " 'cannavale': 18515,\n",
       " 'raped': 3537,\n",
       " \"tutt's\": 52247,\n",
       " 'grasping': 17638,\n",
       " 'despises': 18516,\n",
       " \"thief's\": 40928,\n",
       " 'rapes': 8926,\n",
       " 'raper': 52248,\n",
       " \"eyre'\": 27656,\n",
       " 'walchek': 52249,\n",
       " \"elmo's\": 23386,\n",
       " 'perfumes': 40929,\n",
       " 'spurting': 21918,\n",
       " \"exposition'\\x85\": 52250,\n",
       " 'denoting': 52251,\n",
       " 'thesaurus': 34740,\n",
       " \"shoot'\": 40930,\n",
       " 'bonejack': 49759,\n",
       " 'simpsonian': 52253,\n",
       " 'hebetude': 30601,\n",
       " \"hallow's\": 34741,\n",
       " 'desperation\\x85': 52254,\n",
       " 'incinerator': 34742,\n",
       " 'congratulations': 10308,\n",
       " 'humbled': 52255,\n",
       " \"else's\": 5924,\n",
       " 'trelkovski': 40845,\n",
       " \"rape'\": 52256,\n",
       " \"'chapters'\": 59386,\n",
       " '1600s': 52257,\n",
       " 'martian': 7253,\n",
       " 'nicest': 25256,\n",
       " 'eyred': 52259,\n",
       " 'passenger': 9457,\n",
       " 'disgrace': 6041,\n",
       " 'moderne': 52260,\n",
       " 'barrymore': 5120,\n",
       " 'yankovich': 52261,\n",
       " 'moderns': 40931,\n",
       " 'studliest': 52262,\n",
       " 'bedsheet': 52263,\n",
       " 'decapitation': 14900,\n",
       " 'slurring': 52264,\n",
       " \"'nunsploitation'\": 52265,\n",
       " \"'character'\": 34743,\n",
       " 'cambodia': 9880,\n",
       " 'rebelious': 52266,\n",
       " 'pasadena': 27657,\n",
       " 'crowne': 40932,\n",
       " \"'bedchamber\": 52267,\n",
       " 'conjectural': 52268,\n",
       " 'appologize': 52269,\n",
       " 'halfassing': 52270,\n",
       " 'paycheque': 57816,\n",
       " 'palms': 20606,\n",
       " \"'islands\": 52271,\n",
       " 'hawked': 40933,\n",
       " 'palme': 21919,\n",
       " 'conservatively': 40934,\n",
       " 'larp': 64007,\n",
       " 'palma': 5558,\n",
       " 'smelling': 21920,\n",
       " 'aragorn': 12998,\n",
       " 'hawker': 52272,\n",
       " 'hawkes': 52273,\n",
       " 'explosions': 3975,\n",
       " 'loren': 8059,\n",
       " \"pyle's\": 52274,\n",
       " 'shootout': 6704,\n",
       " \"mike's\": 18517,\n",
       " \"driscoll's\": 52275,\n",
       " 'cogsworth': 40935,\n",
       " \"britian's\": 52276,\n",
       " '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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Loading the word_to_index dictionary\n",
    "\n",
    "word_to_index = imdb.get_word_index()\n",
    "word_to_index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There we have loaded the dictionary. But to visualize one review we need to reverse this dictionary and make the keys as the values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{34701: 'fawn',\n",
       " 52006: 'tsukino',\n",
       " 52007: 'nunnery',\n",
       " 16816: 'sonja',\n",
       " 63951: 'vani',\n",
       " 1408: 'woods',\n",
       " 16115: 'spiders',\n",
       " 2345: 'hanging',\n",
       " 2289: 'woody',\n",
       " 52008: 'trawling',\n",
       " 52009: \"hold's\",\n",
       " 11307: 'comically',\n",
       " 40830: 'localized',\n",
       " 30568: 'disobeying',\n",
       " 52010: \"'royale\",\n",
       " 40831: \"harpo's\",\n",
       " 52011: 'canet',\n",
       " 19313: 'aileen',\n",
       " 52012: 'acurately',\n",
       " 52013: \"diplomat's\",\n",
       " 25242: 'rickman',\n",
       " 6746: 'arranged',\n",
       " 52014: 'rumbustious',\n",
       " 52015: 'familiarness',\n",
       " 52016: \"spider'\",\n",
       " 68804: 'hahahah',\n",
       " 52017: \"wood'\",\n",
       " 40833: 'transvestism',\n",
       " 34702: \"hangin'\",\n",
       " 2338: 'bringing',\n",
       " 40834: 'seamier',\n",
       " 34703: 'wooded',\n",
       " 52018: 'bravora',\n",
       " 16817: 'grueling',\n",
       " 1636: 'wooden',\n",
       " 16818: 'wednesday',\n",
       " 52019: \"'prix\",\n",
       " 34704: 'altagracia',\n",
       " 52020: 'circuitry',\n",
       " 11585: 'crotch',\n",
       " 57766: 'busybody',\n",
       " 52021: \"tart'n'tangy\",\n",
       " 14129: 'burgade',\n",
       " 52023: 'thrace',\n",
       " 11038: \"tom's\",\n",
       " 52025: 'snuggles',\n",
       " 29114: 'francesco',\n",
       " 52027: 'complainers',\n",
       " 52125: 'templarios',\n",
       " 40835: '272',\n",
       " 52028: '273',\n",
       " 52130: 'zaniacs',\n",
       " 34706: '275',\n",
       " 27631: 'consenting',\n",
       " 40836: 'snuggled',\n",
       " 15492: 'inanimate',\n",
       " 52030: 'uality',\n",
       " 11926: 'bronte',\n",
       " 4010: 'errors',\n",
       " 3230: 'dialogs',\n",
       " 52031: \"yomada's\",\n",
       " 34707: \"madman's\",\n",
       " 30585: 'dialoge',\n",
       " 52033: 'usenet',\n",
       " 40837: 'videodrome',\n",
       " 26338: \"kid'\",\n",
       " 52034: 'pawed',\n",
       " 30569: \"'girlfriend'\",\n",
       " 52035: \"'pleasure\",\n",
       " 52036: \"'reloaded'\",\n",
       " 40839: \"kazakos'\",\n",
       " 52037: 'rocque',\n",
       " 52038: 'mailings',\n",
       " 11927: 'brainwashed',\n",
       " 16819: 'mcanally',\n",
       " 52039: \"tom''\",\n",
       " 25243: 'kurupt',\n",
       " 21905: 'affiliated',\n",
       " 52040: 'babaganoosh',\n",
       " 40840: \"noe's\",\n",
       " 40841: 'quart',\n",
       " 359: 'kids',\n",
       " 5034: 'uplifting',\n",
       " 7093: 'controversy',\n",
       " 21906: 'kida',\n",
       " 23379: 'kidd',\n",
       " 52041: \"error'\",\n",
       " 52042: 'neurologist',\n",
       " 18510: 'spotty',\n",
       " 30570: 'cobblers',\n",
       " 9878: 'projection',\n",
       " 40842: 'fastforwarding',\n",
       " 52043: 'sters',\n",
       " 52044: \"eggar's\",\n",
       " 52045: 'etherything',\n",
       " 40843: 'gateshead',\n",
       " 34708: 'airball',\n",
       " 25244: 'unsinkable',\n",
       " 7180: 'stern',\n",
       " 52046: \"cervi's\",\n",
       " 40844: 'dnd',\n",
       " 11586: 'dna',\n",
       " 20598: 'insecurity',\n",
       " 52047: \"'reboot'\",\n",
       " 11037: 'trelkovsky',\n",
       " 52048: 'jaekel',\n",
       " 52049: 'sidebars',\n",
       " 52050: \"sforza's\",\n",
       " 17633: 'distortions',\n",
       " 52051: 'mutinies',\n",
       " 30602: 'sermons',\n",
       " 40846: '7ft',\n",
       " 52052: 'boobage',\n",
       " 52053: \"o'bannon's\",\n",
       " 23380: 'populations',\n",
       " 52054: 'chulak',\n",
       " 27633: 'mesmerize',\n",
       " 52055: 'quinnell',\n",
       " 10307: 'yahoo',\n",
       " 52057: 'meteorologist',\n",
       " 42577: 'beswick',\n",
       " 15493: 'boorman',\n",
       " 40847: 'voicework',\n",
       " 52058: \"ster'\",\n",
       " 22922: 'blustering',\n",
       " 52059: 'hj',\n",
       " 27634: 'intake',\n",
       " 5621: 'morally',\n",
       " 40849: 'jumbling',\n",
       " 52060: 'bowersock',\n",
       " 52061: \"'porky's'\",\n",
       " 16821: 'gershon',\n",
       " 40850: 'ludicrosity',\n",
       " 52062: 'coprophilia',\n",
       " 40851: 'expressively',\n",
       " 19500: \"india's\",\n",
       " 34710: \"post's\",\n",
       " 52063: 'wana',\n",
       " 5283: 'wang',\n",
       " 30571: 'wand',\n",
       " 25245: 'wane',\n",
       " 52321: 'edgeways',\n",
       " 34711: 'titanium',\n",
       " 40852: 'pinta',\n",
       " 178: 'want',\n",
       " 30572: 'pinto',\n",
       " 52065: 'whoopdedoodles',\n",
       " 21908: 'tchaikovsky',\n",
       " 2103: 'travel',\n",
       " 52066: \"'victory'\",\n",
       " 11928: 'copious',\n",
       " 22433: 'gouge',\n",
       " 52067: \"chapters'\",\n",
       " 6702: 'barbra',\n",
       " 30573: 'uselessness',\n",
       " 52068: \"wan'\",\n",
       " 27635: 'assimilated',\n",
       " 16116: 'petiot',\n",
       " 52069: 'most\\x85and',\n",
       " 3930: 'dinosaurs',\n",
       " 352: 'wrong',\n",
       " 52070: 'seda',\n",
       " 52071: 'stollen',\n",
       " 34712: 'sentencing',\n",
       " 40853: 'ouroboros',\n",
       " 40854: 'assimilates',\n",
       " 40855: 'colorfully',\n",
       " 27636: 'glenne',\n",
       " 52072: 'dongen',\n",
       " 4760: 'subplots',\n",
       " 52073: 'kiloton',\n",
       " 23381: 'chandon',\n",
       " 34713: \"effect'\",\n",
       " 27637: 'snugly',\n",
       " 40856: 'kuei',\n",
       " 9092: 'welcomed',\n",
       " 30071: 'dishonor',\n",
       " 52075: 'concurrence',\n",
       " 23382: 'stoicism',\n",
       " 14896: \"guys'\",\n",
       " 52077: \"beroemd'\",\n",
       " 6703: 'butcher',\n",
       " 40857: \"melfi's\",\n",
       " 30623: 'aargh',\n",
       " 20599: 'playhouse',\n",
       " 11308: 'wickedly',\n",
       " 1180: 'fit',\n",
       " 52078: 'labratory',\n",
       " 40859: 'lifeline',\n",
       " 1927: 'screaming',\n",
       " 4287: 'fix',\n",
       " 52079: 'cineliterate',\n",
       " 52080: 'fic',\n",
       " 52081: 'fia',\n",
       " 34714: 'fig',\n",
       " 52082: 'fmvs',\n",
       " 52083: 'fie',\n",
       " 52084: 'reentered',\n",
       " 30574: 'fin',\n",
       " 52085: 'doctresses',\n",
       " 52086: 'fil',\n",
       " 12606: 'zucker',\n",
       " 31931: 'ached',\n",
       " 52088: 'counsil',\n",
       " 52089: 'paterfamilias',\n",
       " 13885: 'songwriter',\n",
       " 34715: 'shivam',\n",
       " 9654: 'hurting',\n",
       " 299: 'effects',\n",
       " 52090: 'slauther',\n",
       " 52091: \"'flame'\",\n",
       " 52092: 'sommerset',\n",
       " 52093: 'interwhined',\n",
       " 27638: 'whacking',\n",
       " 52094: 'bartok',\n",
       " 8775: 'barton',\n",
       " 21909: 'frewer',\n",
       " 52095: \"fi'\",\n",
       " 6192: 'ingrid',\n",
       " 30575: 'stribor',\n",
       " 52096: 'approporiately',\n",
       " 52097: 'wobblyhand',\n",
       " 52098: 'tantalisingly',\n",
       " 52099: 'ankylosaurus',\n",
       " 17634: 'parasites',\n",
       " 52100: 'childen',\n",
       " 52101: \"jenkins'\",\n",
       " 52102: 'metafiction',\n",
       " 17635: 'golem',\n",
       " 40860: 'indiscretion',\n",
       " 23383: \"reeves'\",\n",
       " 57781: \"inamorata's\",\n",
       " 52104: 'brittannica',\n",
       " 7916: 'adapt',\n",
       " 30576: \"russo's\",\n",
       " 48246: 'guitarists',\n",
       " 10553: 'abbott',\n",
       " 40861: 'abbots',\n",
       " 17649: 'lanisha',\n",
       " 40863: 'magickal',\n",
       " 52105: 'mattter',\n",
       " 52106: \"'willy\",\n",
       " 34716: 'pumpkins',\n",
       " 52107: 'stuntpeople',\n",
       " 30577: 'estimate',\n",
       " 40864: 'ugghhh',\n",
       " 11309: 'gameplay',\n",
       " 52108: \"wern't\",\n",
       " 40865: \"n'sync\",\n",
       " 16117: 'sickeningly',\n",
       " 40866: 'chiara',\n",
       " 4011: 'disturbed',\n",
       " 40867: 'portmanteau',\n",
       " 52109: 'ineffectively',\n",
       " 82143: \"duchonvey's\",\n",
       " 37519: \"nasty'\",\n",
       " 1285: 'purpose',\n",
       " 52112: 'lazers',\n",
       " 28105: 'lightened',\n",
       " 52113: 'kaliganj',\n",
       " 52114: 'popularism',\n",
       " 18511: \"damme's\",\n",
       " 30578: 'stylistics',\n",
       " 52115: 'mindgaming',\n",
       " 46449: 'spoilerish',\n",
       " 52117: \"'corny'\",\n",
       " 34718: 'boerner',\n",
       " 6792: 'olds',\n",
       " 52118: 'bakelite',\n",
       " 27639: 'renovated',\n",
       " 27640: 'forrester',\n",
       " 52119: \"lumiere's\",\n",
       " 52024: 'gaskets',\n",
       " 884: 'needed',\n",
       " 34719: 'smight',\n",
       " 1297: 'master',\n",
       " 25905: \"edie's\",\n",
       " 40868: 'seeber',\n",
       " 52120: 'hiya',\n",
       " 52121: 'fuzziness',\n",
       " 14897: 'genesis',\n",
       " 12607: 'rewards',\n",
       " 30579: 'enthrall',\n",
       " 40869: \"'about\",\n",
       " 52122: \"recollection's\",\n",
       " 11039: 'mutilated',\n",
       " 52123: 'fatherlands',\n",
       " 52124: \"fischer's\",\n",
       " 5399: 'positively',\n",
       " 34705: '270',\n",
       " 34720: 'ahmed',\n",
       " 9836: 'zatoichi',\n",
       " 13886: 'bannister',\n",
       " 52127: 'anniversaries',\n",
       " 30580: \"helm's\",\n",
       " 52128: \"'work'\",\n",
       " 34721: 'exclaimed',\n",
       " 52129: \"'unfunny'\",\n",
       " 52029: '274',\n",
       " 544: 'feeling',\n",
       " 52131: \"wanda's\",\n",
       " 33266: 'dolan',\n",
       " 52133: '278',\n",
       " 52134: 'peacoat',\n",
       " 40870: 'brawny',\n",
       " 40871: 'mishra',\n",
       " 40872: 'worlders',\n",
       " 52135: 'protags',\n",
       " 52136: 'skullcap',\n",
       " 57596: 'dastagir',\n",
       " 5622: 'affairs',\n",
       " 7799: 'wholesome',\n",
       " 52137: 'hymen',\n",
       " 25246: 'paramedics',\n",
       " 52138: 'unpersons',\n",
       " 52139: 'heavyarms',\n",
       " 52140: 'affaire',\n",
       " 52141: 'coulisses',\n",
       " 40873: 'hymer',\n",
       " 52142: 'kremlin',\n",
       " 30581: 'shipments',\n",
       " 52143: 'pixilated',\n",
       " 30582: \"'00s\",\n",
       " 18512: 'diminishing',\n",
       " 1357: 'cinematic',\n",
       " 14898: 'resonates',\n",
       " 40874: 'simplify',\n",
       " 40875: \"nature'\",\n",
       " 40876: 'temptresses',\n",
       " 16822: 'reverence',\n",
       " 19502: 'resonated',\n",
       " 34722: 'dailey',\n",
       " 52144: '2\\x85',\n",
       " 27641: 'treize',\n",
       " 52145: 'majo',\n",
       " 21910: 'kiya',\n",
       " 52146: 'woolnough',\n",
       " 39797: 'thanatos',\n",
       " 35731: 'sandoval',\n",
       " 40879: 'dorama',\n",
       " 52147: \"o'shaughnessy\",\n",
       " 4988: 'tech',\n",
       " 32018: 'fugitives',\n",
       " 30583: 'teck',\n",
       " 76125: \"'e'\",\n",
       " 40881: 'doesn’t',\n",
       " 52149: 'purged',\n",
       " 657: 'saying',\n",
       " 41095: \"martians'\",\n",
       " 23418: 'norliss',\n",
       " 27642: 'dickey',\n",
       " 52152: 'dicker',\n",
       " 52153: \"'sependipity\",\n",
       " 8422: 'padded',\n",
       " 57792: 'ordell',\n",
       " 40882: \"sturges'\",\n",
       " 52154: 'independentcritics',\n",
       " 5745: 'tempted',\n",
       " 34724: \"atkinson's\",\n",
       " 25247: 'hounded',\n",
       " 52155: 'apace',\n",
       " 15494: 'clicked',\n",
       " 30584: \"'humor'\",\n",
       " 17177: \"martino's\",\n",
       " 52156: \"'supporting\",\n",
       " 52032: 'warmongering',\n",
       " 34725: \"zemeckis's\",\n",
       " 21911: 'lube',\n",
       " 52157: 'shocky',\n",
       " 7476: 'plate',\n",
       " 40883: 'plata',\n",
       " 40884: 'sturgess',\n",
       " 40885: \"nerds'\",\n",
       " 20600: 'plato',\n",
       " 34726: 'plath',\n",
       " 40886: 'platt',\n",
       " 52159: 'mcnab',\n",
       " 27643: 'clumsiness',\n",
       " 3899: 'altogether',\n",
       " 42584: 'massacring',\n",
       " 52160: 'bicenntinial',\n",
       " 40887: 'skaal',\n",
       " 14360: 'droning',\n",
       " 8776: 'lds',\n",
       " 21912: 'jaguar',\n",
       " 34727: \"cale's\",\n",
       " 1777: 'nicely',\n",
       " 4588: 'mummy',\n",
       " 18513: \"lot's\",\n",
       " 10086: 'patch',\n",
       " 50202: 'kerkhof',\n",
       " 52161: \"leader's\",\n",
       " 27644: \"'movie\",\n",
       " 52162: 'uncomfirmed',\n",
       " 40888: 'heirloom',\n",
       " 47360: 'wrangle',\n",
       " 52163: 'emotion\\x85',\n",
       " 52164: \"'stargate'\",\n",
       " 40889: 'pinoy',\n",
       " 40890: 'conchatta',\n",
       " 41128: 'broeke',\n",
       " 40891: 'advisedly',\n",
       " 17636: \"barker's\",\n",
       " 52166: 'descours',\n",
       " 772: 'lots',\n",
       " 9259: 'lotr',\n",
       " 9879: 'irs',\n",
       " 52167: 'lott',\n",
       " 40892: 'xvi',\n",
       " 34728: 'irk',\n",
       " 52168: 'irl',\n",
       " 6887: 'ira',\n",
       " 21913: 'belzer',\n",
       " 52169: 'irc',\n",
       " 27645: 'ire',\n",
       " 40893: 'requisites',\n",
       " 7693: 'discipline',\n",
       " 52961: 'lyoko',\n",
       " 11310: 'extend',\n",
       " 873: 'nature',\n",
       " 52170: \"'dickie'\",\n",
       " 40894: 'optimist',\n",
       " 30586: 'lapping',\n",
       " 3900: 'superficial',\n",
       " 52171: 'vestment',\n",
       " 2823: 'extent',\n",
       " 52172: 'tendons',\n",
       " 52173: \"heller's\",\n",
       " 52174: 'quagmires',\n",
       " 52175: 'miyako',\n",
       " 20601: 'moocow',\n",
       " 52176: \"coles'\",\n",
       " 40895: 'lookit',\n",
       " 52177: 'ravenously',\n",
       " 40896: 'levitating',\n",
       " 52178: 'perfunctorily',\n",
       " 30587: 'lookin',\n",
       " 40898: \"lot'\",\n",
       " 52179: 'lookie',\n",
       " 34870: 'fearlessly',\n",
       " 52181: 'libyan',\n",
       " 40899: 'fondles',\n",
       " 35714: 'gopher',\n",
       " 40901: 'wearying',\n",
       " 52182: \"nz's\",\n",
       " 27646: 'minuses',\n",
       " 52183: 'puposelessly',\n",
       " 52184: 'shandling',\n",
       " 31268: 'decapitates',\n",
       " 11929: 'humming',\n",
       " 40902: \"'nother\",\n",
       " 21914: 'smackdown',\n",
       " 30588: 'underdone',\n",
       " 40903: 'frf',\n",
       " 52185: 'triviality',\n",
       " 25248: 'fro',\n",
       " 8777: 'bothers',\n",
       " 52186: \"'kensington\",\n",
       " 73: 'much',\n",
       " 34730: 'muco',\n",
       " 22615: 'wiseguy',\n",
       " 27648: \"richie's\",\n",
       " 40904: 'tonino',\n",
       " 52187: 'unleavened',\n",
       " 11587: 'fry',\n",
       " 40905: \"'tv'\",\n",
       " 40906: 'toning',\n",
       " 14361: 'obese',\n",
       " 30589: 'sensationalized',\n",
       " 40907: 'spiv',\n",
       " 6259: 'spit',\n",
       " 7364: 'arkin',\n",
       " 21915: 'charleton',\n",
       " 16823: 'jeon',\n",
       " 21916: 'boardroom',\n",
       " 4989: 'doubts',\n",
       " 3084: 'spin',\n",
       " 53083: 'hepo',\n",
       " 27649: 'wildcat',\n",
       " 10584: 'venoms',\n",
       " 52191: 'misconstrues',\n",
       " 18514: 'mesmerising',\n",
       " 40908: 'misconstrued',\n",
       " 52192: 'rescinds',\n",
       " 52193: 'prostrate',\n",
       " 40909: 'majid',\n",
       " 16479: 'climbed',\n",
       " 34731: 'canoeing',\n",
       " 52195: 'majin',\n",
       " 57804: 'animie',\n",
       " 40910: 'sylke',\n",
       " 14899: 'conditioned',\n",
       " 40911: 'waddell',\n",
       " 52196: '3\\x85',\n",
       " 41188: 'hyperdrive',\n",
       " 34732: 'conditioner',\n",
       " 53153: 'bricklayer',\n",
       " 2576: 'hong',\n",
       " 52198: 'memoriam',\n",
       " 30592: 'inventively',\n",
       " 25249: \"levant's\",\n",
       " 20638: 'portobello',\n",
       " 52200: 'remand',\n",
       " 19504: 'mummified',\n",
       " 27650: 'honk',\n",
       " 19505: 'spews',\n",
       " 40912: 'visitations',\n",
       " 52201: 'mummifies',\n",
       " 25250: 'cavanaugh',\n",
       " 23385: 'zeon',\n",
       " 40913: \"jungle's\",\n",
       " 34733: 'viertel',\n",
       " 27651: 'frenchmen',\n",
       " 52202: 'torpedoes',\n",
       " 52203: 'schlessinger',\n",
       " 34734: 'torpedoed',\n",
       " 69876: 'blister',\n",
       " 52204: 'cinefest',\n",
       " 34735: 'furlough',\n",
       " 52205: 'mainsequence',\n",
       " 40914: 'mentors',\n",
       " 9094: 'academic',\n",
       " 20602: 'stillness',\n",
       " 40915: 'academia',\n",
       " 52206: 'lonelier',\n",
       " 52207: 'nibby',\n",
       " 52208: \"losers'\",\n",
       " 40916: 'cineastes',\n",
       " 4449: 'corporate',\n",
       " 40917: 'massaging',\n",
       " 30593: 'bellow',\n",
       " 19506: 'absurdities',\n",
       " 53241: 'expetations',\n",
       " 40918: 'nyfiken',\n",
       " 75638: 'mehras',\n",
       " 52209: 'lasse',\n",
       " 52210: 'visability',\n",
       " 33946: 'militarily',\n",
       " 52211: \"elder'\",\n",
       " 19023: 'gainsbourg',\n",
       " 20603: 'hah',\n",
       " 13420: 'hai',\n",
       " 34736: 'haj',\n",
       " 25251: 'hak',\n",
       " 4311: 'hal',\n",
       " 4892: 'ham',\n",
       " 53259: 'duffer',\n",
       " 52213: 'haa',\n",
       " 66: 'had',\n",
       " 11930: 'advancement',\n",
       " 16825: 'hag',\n",
       " 25252: \"hand'\",\n",
       " 13421: 'hay',\n",
       " 20604: 'mcnamara',\n",
       " 52214: \"mozart's\",\n",
       " 30731: 'duffel',\n",
       " 30594: 'haq',\n",
       " 13887: 'har',\n",
       " 44: 'has',\n",
       " 2401: 'hat',\n",
       " 40919: 'hav',\n",
       " 30595: 'haw',\n",
       " 52215: 'figtings',\n",
       " 15495: 'elders',\n",
       " 52216: 'underpanted',\n",
       " 52217: 'pninson',\n",
       " 27652: 'unequivocally',\n",
       " 23673: \"barbara's\",\n",
       " 52219: \"bello'\",\n",
       " 12997: 'indicative',\n",
       " 40920: 'yawnfest',\n",
       " 52220: 'hexploitation',\n",
       " 52221: \"loder's\",\n",
       " 27653: 'sleuthing',\n",
       " 32622: \"justin's\",\n",
       " 52222: \"'ball\",\n",
       " 52223: \"'summer\",\n",
       " 34935: \"'demons'\",\n",
       " 52225: \"mormon's\",\n",
       " 34737: \"laughton's\",\n",
       " 52226: 'debell',\n",
       " 39724: 'shipyard',\n",
       " 30597: 'unabashedly',\n",
       " 40401: 'disks',\n",
       " 2290: 'crowd',\n",
       " 10087: 'crowe',\n",
       " 56434: \"vancouver's\",\n",
       " 34738: 'mosques',\n",
       " 6627: 'crown',\n",
       " 52227: 'culpas',\n",
       " 27654: 'crows',\n",
       " 53344: 'surrell',\n",
       " 52229: 'flowless',\n",
       " 52230: 'sheirk',\n",
       " 40923: \"'three\",\n",
       " 52231: \"peterson'\",\n",
       " 52232: 'ooverall',\n",
       " 40924: 'perchance',\n",
       " 1321: 'bottom',\n",
       " 53363: 'chabert',\n",
       " 52233: 'sneha',\n",
       " 13888: 'inhuman',\n",
       " 52234: 'ichii',\n",
       " 52235: 'ursla',\n",
       " 30598: 'completly',\n",
       " 40925: 'moviedom',\n",
       " 52236: 'raddick',\n",
       " 51995: 'brundage',\n",
       " 40926: 'brigades',\n",
       " 1181: 'starring',\n",
       " 52237: \"'goal'\",\n",
       " 52238: 'caskets',\n",
       " 52239: 'willcock',\n",
       " 52240: \"threesome's\",\n",
       " 52241: \"mosque'\",\n",
       " 52242: \"cover's\",\n",
       " 17637: 'spaceships',\n",
       " 40927: 'anomalous',\n",
       " 27655: 'ptsd',\n",
       " 52243: 'shirdan',\n",
       " 21962: 'obscenity',\n",
       " 30599: 'lemmings',\n",
       " 30600: 'duccio',\n",
       " 52244: \"levene's\",\n",
       " 52245: \"'gorby'\",\n",
       " 25255: \"teenager's\",\n",
       " 5340: 'marshall',\n",
       " 9095: 'honeymoon',\n",
       " 3231: 'shoots',\n",
       " 12258: 'despised',\n",
       " 52246: 'okabasho',\n",
       " 8289: 'fabric',\n",
       " 18515: 'cannavale',\n",
       " 3537: 'raped',\n",
       " 52247: \"tutt's\",\n",
       " 17638: 'grasping',\n",
       " 18516: 'despises',\n",
       " 40928: \"thief's\",\n",
       " 8926: 'rapes',\n",
       " 52248: 'raper',\n",
       " 27656: \"eyre'\",\n",
       " 52249: 'walchek',\n",
       " 23386: \"elmo's\",\n",
       " 40929: 'perfumes',\n",
       " 21918: 'spurting',\n",
       " 52250: \"exposition'\\x85\",\n",
       " 52251: 'denoting',\n",
       " 34740: 'thesaurus',\n",
       " 40930: \"shoot'\",\n",
       " 49759: 'bonejack',\n",
       " 52253: 'simpsonian',\n",
       " 30601: 'hebetude',\n",
       " 34741: \"hallow's\",\n",
       " 52254: 'desperation\\x85',\n",
       " 34742: 'incinerator',\n",
       " 10308: 'congratulations',\n",
       " 52255: 'humbled',\n",
       " 5924: \"else's\",\n",
       " 40845: 'trelkovski',\n",
       " 52256: \"rape'\",\n",
       " 59386: \"'chapters'\",\n",
       " 52257: '1600s',\n",
       " 7253: 'martian',\n",
       " 25256: 'nicest',\n",
       " 52259: 'eyred',\n",
       " 9457: 'passenger',\n",
       " 6041: 'disgrace',\n",
       " 52260: 'moderne',\n",
       " 5120: 'barrymore',\n",
       " 52261: 'yankovich',\n",
       " 40931: 'moderns',\n",
       " 52262: 'studliest',\n",
       " 52263: 'bedsheet',\n",
       " 14900: 'decapitation',\n",
       " 52264: 'slurring',\n",
       " 52265: \"'nunsploitation'\",\n",
       " 34743: \"'character'\",\n",
       " 9880: 'cambodia',\n",
       " 52266: 'rebelious',\n",
       " 27657: 'pasadena',\n",
       " 40932: 'crowne',\n",
       " 52267: \"'bedchamber\",\n",
       " 52268: 'conjectural',\n",
       " 52269: 'appologize',\n",
       " 52270: 'halfassing',\n",
       " 57816: 'paycheque',\n",
       " 20606: 'palms',\n",
       " 52271: \"'islands\",\n",
       " 40933: 'hawked',\n",
       " 21919: 'palme',\n",
       " 40934: 'conservatively',\n",
       " 64007: 'larp',\n",
       " 5558: 'palma',\n",
       " 21920: 'smelling',\n",
       " 12998: 'aragorn',\n",
       " 52272: 'hawker',\n",
       " 52273: 'hawkes',\n",
       " 3975: 'explosions',\n",
       " 8059: 'loren',\n",
       " 52274: \"pyle's\",\n",
       " 6704: 'shootout',\n",
       " 18517: \"mike's\",\n",
       " 52275: \"driscoll's\",\n",
       " 40935: 'cogsworth',\n",
       " 52276: \"britian's\",\n",
       " 34744: 'childs',\n",
       " 52277: \"portrait's\",\n",
       " 3626: 'chain',\n",
       " 2497: 'whoever',\n",
       " 52278: 'puttered',\n",
       " 52279: 'childe',\n",
       " 52280: 'maywether',\n",
       " 3036: 'chair',\n",
       " 52281: \"rance's\",\n",
       " 34745: 'machu',\n",
       " 4517: 'ballet',\n",
       " 34746: 'grapples',\n",
       " 76152: 'summerize',\n",
       " 30603: 'freelance',\n",
       " 52283: \"andrea's\",\n",
       " 52284: '\\x91very',\n",
       " 45879: 'coolidge',\n",
       " 18518: 'mache',\n",
       " 52285: 'balled',\n",
       " 40937: 'grappled',\n",
       " 18519: 'macha',\n",
       " 21921: 'underlining',\n",
       " 5623: 'macho',\n",
       " 19507: 'oversight',\n",
       " 25257: 'machi',\n",
       " 11311: 'verbally',\n",
       " 21922: 'tenacious',\n",
       " 40938: 'windshields',\n",
       " 18557: 'paychecks',\n",
       " 3396: 'jerk',\n",
       " 11931: \"good'\",\n",
       " 34748: 'prancer',\n",
       " 21923: 'prances',\n",
       " 52286: 'olympus',\n",
       " 21924: 'lark',\n",
       " 10785: 'embark',\n",
       " 7365: 'gloomy',\n",
       " 52287: 'jehaan',\n",
       " 52288: 'turaqui',\n",
       " 20607: \"child'\",\n",
       " 2894: 'locked',\n",
       " 52289: 'pranced',\n",
       " 2588: 'exact',\n",
       " 52290: 'unattuned',\n",
       " 783: 'minute',\n",
       " 16118: 'skewed',\n",
       " 40940: 'hodgins',\n",
       " 34749: 'skewer',\n",
       " 52291: 'think\\x85',\n",
       " 38765: 'rosenstein',\n",
       " 52292: 'helmit',\n",
       " 34750: 'wrestlemanias',\n",
       " 16826: 'hindered',\n",
       " 30604: \"martha's\",\n",
       " 52293: 'cheree',\n",
       " 52294: \"pluckin'\",\n",
       " 40941: 'ogles',\n",
       " 11932: 'heavyweight',\n",
       " 82190: 'aada',\n",
       " 11312: 'chopping',\n",
       " 61534: 'strongboy',\n",
       " 41342: 'hegemonic',\n",
       " 40942: 'adorns',\n",
       " 41346: 'xxth',\n",
       " 34751: 'nobuhiro',\n",
       " 52298: 'capitães',\n",
       " 52299: 'kavogianni',\n",
       " 13422: 'antwerp',\n",
       " 6538: 'celebrated',\n",
       " 52300: 'roarke',\n",
       " 40943: 'baggins',\n",
       " 31270: 'cheeseburgers',\n",
       " 52301: 'matras',\n",
       " 52302: \"nineties'\",\n",
       " 52303: \"'craig'\",\n",
       " 12999: 'celebrates',\n",
       " 3383: 'unintentionally',\n",
       " 14362: 'drafted',\n",
       " 52304: 'climby',\n",
       " 52305: '303',\n",
       " 18520: 'oldies',\n",
       " 9096: 'climbs',\n",
       " 9655: 'honour',\n",
       " 34752: 'plucking',\n",
       " 30074: '305',\n",
       " 5514: 'address',\n",
       " 40944: 'menjou',\n",
       " 42592: \"'freak'\",\n",
       " 19508: 'dwindling',\n",
       " 9458: 'benson',\n",
       " 52307: 'white’s',\n",
       " 40945: 'shamelessness',\n",
       " 21925: 'impacted',\n",
       " 52308: 'upatz',\n",
       " 3840: 'cusack',\n",
       " 37567: \"flavia's\",\n",
       " 52309: 'effette',\n",
       " 34753: 'influx',\n",
       " 52310: 'boooooooo',\n",
       " 52311: 'dimitrova',\n",
       " 13423: 'houseman',\n",
       " 25259: 'bigas',\n",
       " 52312: 'boylen',\n",
       " 52313: 'phillipenes',\n",
       " 40946: 'fakery',\n",
       " 27658: \"grandpa's\",\n",
       " 27659: 'darnell',\n",
       " 19509: 'undergone',\n",
       " 52315: 'handbags',\n",
       " 21926: 'perished',\n",
       " 37778: 'pooped',\n",
       " 27660: 'vigour',\n",
       " 3627: 'opposed',\n",
       " 52316: 'etude',\n",
       " 11799: \"caine's\",\n",
       " 52317: 'doozers',\n",
       " 34754: 'photojournals',\n",
       " 52318: 'perishes',\n",
       " 34755: 'constrains',\n",
       " 40948: 'migenes',\n",
       " 30605: 'consoled',\n",
       " 16827: 'alastair',\n",
       " 52319: 'wvs',\n",
       " 52320: 'ooooooh',\n",
       " 34756: 'approving',\n",
       " 40949: 'consoles',\n",
       " 52064: 'disparagement',\n",
       " 52322: 'futureistic',\n",
       " 52323: 'rebounding',\n",
       " 52324: \"'date\",\n",
       " 52325: 'gregoire',\n",
       " 21927: 'rutherford',\n",
       " 34757: 'americanised',\n",
       " 82196: 'novikov',\n",
       " 1042: 'following',\n",
       " 34758: 'munroe',\n",
       " 52326: \"morita'\",\n",
       " 52327: 'christenssen',\n",
       " 23106: 'oatmeal',\n",
       " 25260: 'fossey',\n",
       " 40950: 'livered',\n",
       " 13000: 'listens',\n",
       " 76164: \"'marci\",\n",
       " 52330: \"otis's\",\n",
       " 23387: 'thanking',\n",
       " 16019: 'maude',\n",
       " 34759: 'extensions',\n",
       " 52332: 'ameteurish',\n",
       " 52333: \"commender's\",\n",
       " 27661: 'agricultural',\n",
       " 4518: 'convincingly',\n",
       " 17639: 'fueled',\n",
       " 54014: 'mahattan',\n",
       " 40952: \"paris's\",\n",
       " 52336: 'vulkan',\n",
       " 52337: 'stapes',\n",
       " 52338: 'odysessy',\n",
       " 12259: 'harmon',\n",
       " 4252: 'surfing',\n",
       " 23494: 'halloran',\n",
       " 49580: 'unbelieveably',\n",
       " 52339: \"'offed'\",\n",
       " 30607: 'quadrant',\n",
       " 19510: 'inhabiting',\n",
       " 34760: 'nebbish',\n",
       " 40953: 'forebears',\n",
       " 34761: 'skirmish',\n",
       " 52340: 'ocassionally',\n",
       " 52341: \"'resist\",\n",
       " 21928: 'impactful',\n",
       " 52342: 'spicier',\n",
       " 40954: 'touristy',\n",
       " 52343: \"'football'\",\n",
       " 40955: 'webpage',\n",
       " 52345: 'exurbia',\n",
       " 52346: 'jucier',\n",
       " 14901: 'professors',\n",
       " 34762: 'structuring',\n",
       " 30608: 'jig',\n",
       " 40956: 'overlord',\n",
       " 25261: 'disconnect',\n",
       " 82201: 'sniffle',\n",
       " 40957: 'slimeball',\n",
       " 40958: 'jia',\n",
       " 16828: 'milked',\n",
       " 40959: 'banjoes',\n",
       " 1237: 'jim',\n",
       " 52348: 'workforces',\n",
       " 52349: 'jip',\n",
       " 52350: 'rotweiller',\n",
       " 34763: 'mundaneness',\n",
       " 52351: \"'ninja'\",\n",
       " 11040: \"dead'\",\n",
       " 40960: \"cipriani's\",\n",
       " 20608: 'modestly',\n",
       " 52352: \"professor'\",\n",
       " 40961: 'shacked',\n",
       " 34764: 'bashful',\n",
       " 23388: 'sorter',\n",
       " 16120: 'overpowering',\n",
       " 18521: 'workmanlike',\n",
       " 27662: 'henpecked',\n",
       " 18522: 'sorted',\n",
       " 52354: \"jōb's\",\n",
       " 52355: \"'always\",\n",
       " 34765: \"'baptists\",\n",
       " 52356: 'dreamcatchers',\n",
       " 52357: \"'silence'\",\n",
       " 21929: 'hickory',\n",
       " 52358: 'fun\\x97yet',\n",
       " 52359: 'breakumentary',\n",
       " 15496: 'didn',\n",
       " 52360: 'didi',\n",
       " 52361: 'pealing',\n",
       " 40962: 'dispite',\n",
       " 25262: \"italy's\",\n",
       " 21930: 'instability',\n",
       " 6539: 'quarter',\n",
       " 12608: 'quartet',\n",
       " 52362: 'padmé',\n",
       " 52363: \"'bleedmedry\",\n",
       " 52364: 'pahalniuk',\n",
       " 52365: 'honduras',\n",
       " 10786: 'bursting',\n",
       " 41465: \"pablo's\",\n",
       " 52367: 'irremediably',\n",
       " 40963: 'presages',\n",
       " 57832: 'bowlegged',\n",
       " 65183: 'dalip',\n",
       " 6260: 'entering',\n",
       " 76172: 'newsradio',\n",
       " 54150: 'presaged',\n",
       " 27663: \"giallo's\",\n",
       " 40964: 'bouyant',\n",
       " 52368: 'amerterish',\n",
       " 18523: 'rajni',\n",
       " 30610: 'leeves',\n",
       " 34767: 'macauley',\n",
       " 612: 'seriously',\n",
       " 52369: 'sugercoma',\n",
       " 52370: 'grimstead',\n",
       " 52371: \"'fairy'\",\n",
       " 30611: 'zenda',\n",
       " 52372: \"'twins'\",\n",
       " 17640: 'realisation',\n",
       " 27664: 'highsmith',\n",
       " 7817: 'raunchy',\n",
       " 40965: 'incentives',\n",
       " 52374: 'flatson',\n",
       " 35097: 'snooker',\n",
       " 16829: 'crazies',\n",
       " 14902: 'crazier',\n",
       " 7094: 'grandma',\n",
       " 52375: 'napunsaktha',\n",
       " 30612: 'workmanship',\n",
       " 52376: 'reisner',\n",
       " 61306: \"sanford's\",\n",
       " 52377: '\\x91doña',\n",
       " 6108: 'modest',\n",
       " 19153: \"everything's\",\n",
       " 40966: 'hamer',\n",
       " 52379: \"couldn't'\",\n",
       " 13001: 'quibble',\n",
       " 52380: 'socking',\n",
       " 21931: 'tingler',\n",
       " 52381: 'gutman',\n",
       " 40967: 'lachlan',\n",
       " 52382: 'tableaus',\n",
       " 52383: 'headbanger',\n",
       " 2847: 'spoken',\n",
       " 34768: 'cerebrally',\n",
       " 23490: \"'road\",\n",
       " 21932: 'tableaux',\n",
       " 40968: \"proust's\",\n",
       " 40969: 'periodical',\n",
       " 52385: \"shoveller's\",\n",
       " 25263: 'tamara',\n",
       " 17641: 'affords',\n",
       " 3249: 'concert',\n",
       " 87955: \"yara's\",\n",
       " 52386: 'someome',\n",
       " 8424: 'lingering',\n",
       " 41511: \"abraham's\",\n",
       " 34769: 'beesley',\n",
       " 34770: 'cherbourg',\n",
       " 28624: 'kagan',\n",
       " 9097: 'snatch',\n",
       " 9260: \"miyazaki's\",\n",
       " 25264: 'absorbs',\n",
       " 40970: \"koltai's\",\n",
       " 64027: 'tingled',\n",
       " 19511: 'crossroads',\n",
       " 16121: 'rehab',\n",
       " 52389: 'falworth',\n",
       " 52390: 'sequals',\n",
       " ...}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index_to_word = {value : key for (key,value) in word_to_index.items()} #inverting the dictionary word_to_index\n",
    "index_to_word"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"? this film was just brilliant casting location scenery story direction everyone's really suited the part they played and you could just imagine being there robert ? is an amazing actor and now the same being director ? father came from the same scottish island as myself so i loved the fact there was a real connection with this film the witty remarks throughout the film were great it was just brilliant so much that i bought the film as soon as it was released for ? and would recommend it to everyone to watch and the fly fishing was amazing really cried at the end it was so sad and you know what they say if you cry at a film it must have been good and this definitely was also ? to the two little boy's that played the ? of norman and paul they were just brilliant children are often left out of the ? list i think because the stars that play them all grown up are such a big profile for the whole film but these children are amazing and should be praised for what they have done don't you think the whole story was so lovely because it was true and was someone's life after all that was shared with us all\""
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decoded_review = ' '.join([index_to_word.get(i - 3 , '?') for i in review_train[0]]) #getting the exact review\n",
    "decoded_review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def vectorize_sequence(sequences , dimension = 10000): #function to transfer the values into one got vector\n",
    "    \n",
    "    '''\n",
    "    Returns a one hot vector of the sequences of the dimension.\n",
    "    '''\n",
    "    result = np.zeros((len(sequences) , dimension))\n",
    "    \n",
    "    for i,sequence in enumerate(sequences):\n",
    "        result[i , sequence] = 1\n",
    "    \n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = vectorize_sequence(review_train , dimension = 10000) #one hot encoding of the training data\n",
    "X_test = vectorize_sequence(review_train , dimension = 10000) #one hot encoding of test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 1., 1., ..., 0., 0., 0.])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[0] #checking one of the training example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Its always better to keep a set of values for validation set. We are going to keep a 10,000 values from training set for validation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_val = X_train[:10000] # setting the feature values for validation\n",
    "y_val = y_train[:10000] # setting the label values for validation\n",
    "\n",
    "partial_X_train = X_train[10000:] #setting the feature values for training\n",
    "partial_y_train = y_train[10000:] #setting the label values for training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are all set now, to feed our data into our model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3: Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the Neural Network we are going to following Neural Network architecture:\n",
    "\n",
    "1. Dense Layer with 16 activation units of relu\n",
    "2. Dense Layer with 16 activation units of relu\n",
    "3. Dense Layer with 1 activation units of sigmoid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Building our model\n",
    "model = Sequential()\n",
    "\n",
    "model.add(Dense(16 , activation = 'relu' , input_shape = (10000 , )))\n",
    "model.add(Dense(16 , activation = 'relu'))\n",
    "model.add(Dense(1 , activation = 'sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                (None, 16)                160016    \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 16)                272       \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 17        \n",
      "=================================================================\n",
      "Total params: 160,305\n",
      "Trainable params: 160,305\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary() #summarizing and checking the number of parameters of the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer = 'rmsprop',\n",
    "              loss = 'binary_crossentropy',\n",
    "              metrics = ['accuracy']) #compiling our model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 15000 samples, validate on 10000 samples\n",
      "Epoch 1/20\n",
      "15000/15000 [==============================] - 5s 318us/sample - loss: 0.3905 - accuracy: 0.8443 - val_loss: 0.2825 - val_accuracy: 0.8885\n",
      "Epoch 2/20\n",
      "15000/15000 [==============================] - 2s 109us/sample - loss: 0.2080 - accuracy: 0.9235 - val_loss: 0.2809 - val_accuracy: 0.8875\n",
      "Epoch 3/20\n",
      "15000/15000 [==============================] - 2s 102us/sample - loss: 0.1531 - accuracy: 0.9445 - val_loss: 0.2999 - val_accuracy: 0.8827\n",
      "Epoch 4/20\n",
      "15000/15000 [==============================] - 2s 103us/sample - loss: 0.1163 - accuracy: 0.9593 - val_loss: 0.3367 - val_accuracy: 0.8809\n",
      "Epoch 5/20\n",
      "15000/15000 [==============================] - 1s 98us/sample - loss: 0.0908 - accuracy: 0.9693 - val_loss: 0.3888 - val_accuracy: 0.8755\n",
      "Epoch 6/20\n",
      "15000/15000 [==============================] - 1s 100us/sample - loss: 0.0685 - accuracy: 0.9763 - val_loss: 0.4398 - val_accuracy: 0.8720\n",
      "Epoch 7/20\n",
      "15000/15000 [==============================] - 1s 96us/sample - loss: 0.0519 - accuracy: 0.9832 - val_loss: 0.4895 - val_accuracy: 0.8728\n",
      "Epoch 8/20\n",
      "15000/15000 [==============================] - 1s 99us/sample - loss: 0.0375 - accuracy: 0.9869 - val_loss: 0.5640 - val_accuracy: 0.8691\n",
      "Epoch 9/20\n",
      "15000/15000 [==============================] - 1s 98us/sample - loss: 0.0273 - accuracy: 0.9921 - val_loss: 0.6409 - val_accuracy: 0.8670\n",
      "Epoch 10/20\n",
      "15000/15000 [==============================] - 1s 97us/sample - loss: 0.0198 - accuracy: 0.9946 - val_loss: 0.7108 - val_accuracy: 0.8651\n",
      "Epoch 11/20\n",
      "15000/15000 [==============================] - 2s 102us/sample - loss: 0.0122 - accuracy: 0.9971 - val_loss: 0.8059 - val_accuracy: 0.8621\n",
      "Epoch 12/20\n",
      "15000/15000 [==============================] - 1s 96us/sample - loss: 0.0080 - accuracy: 0.9983 - val_loss: 0.9442 - val_accuracy: 0.8574\n",
      "Epoch 13/20\n",
      "15000/15000 [==============================] - 1s 100us/sample - loss: 0.0053 - accuracy: 0.9989 - val_loss: 1.0361 - val_accuracy: 0.8588\n",
      "Epoch 14/20\n",
      "15000/15000 [==============================] - 1s 98us/sample - loss: 0.0037 - accuracy: 0.9996 - val_loss: 1.1421 - val_accuracy: 0.8546\n",
      "Epoch 15/20\n",
      "15000/15000 [==============================] - 1s 98us/sample - loss: 0.0018 - accuracy: 0.9997 - val_loss: 1.2020 - val_accuracy: 0.8584\n",
      "Epoch 16/20\n",
      "15000/15000 [==============================] - 1s 98us/sample - loss: 0.0010 - accuracy: 0.9999 - val_loss: 1.3649 - val_accuracy: 0.8525\n",
      "Epoch 17/20\n",
      "15000/15000 [==============================] - 2s 109us/sample - loss: 7.2372e-04 - accuracy: 0.9998 - val_loss: 1.4090 - val_accuracy: 0.8576\n",
      "Epoch 18/20\n",
      "15000/15000 [==============================] - 1s 94us/sample - loss: 3.8089e-04 - accuracy: 0.9999 - val_loss: 1.5468 - val_accuracy: 0.8572\n",
      "Epoch 19/20\n",
      "15000/15000 [==============================] - 1s 97us/sample - loss: 2.6484e-04 - accuracy: 0.9999 - val_loss: 1.6667 - val_accuracy: 0.8565\n",
      "Epoch 20/20\n",
      "15000/15000 [==============================] - 1s 94us/sample - loss: 8.5961e-04 - accuracy: 0.9997 - val_loss: 1.7249 - val_accuracy: 0.8560\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(partial_X_train,\n",
    "                    partial_y_train,\n",
    "                    epochs = 20,\n",
    "                    batch_size = 128,\n",
    "                    validation_data = (X_val , y_val),\n",
    "                    ) #training our data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>val_loss</th>\n",
       "      <th>val_accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.390484</td>\n",
       "      <td>0.844267</td>\n",
       "      <td>0.282541</td>\n",
       "      <td>0.8885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.207966</td>\n",
       "      <td>0.923533</td>\n",
       "      <td>0.280940</td>\n",
       "      <td>0.8875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.153051</td>\n",
       "      <td>0.944467</td>\n",
       "      <td>0.299931</td>\n",
       "      <td>0.8827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.116307</td>\n",
       "      <td>0.959333</td>\n",
       "      <td>0.336691</td>\n",
       "      <td>0.8809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.090772</td>\n",
       "      <td>0.969267</td>\n",
       "      <td>0.388809</td>\n",
       "      <td>0.8755</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       loss  accuracy  val_loss  val_accuracy\n",
       "0  0.390484  0.844267  0.282541        0.8885\n",
       "1  0.207966  0.923533  0.280940        0.8875\n",
       "2  0.153051  0.944467  0.299931        0.8827\n",
       "3  0.116307  0.959333  0.336691        0.8809\n",
       "4  0.090772  0.969267  0.388809        0.8755"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(history.history)\n",
    "\n",
    "df.head() #making our loss and accuracy dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Its always better to visualize the loss and accuracy to move ahead. So, lets do that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "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": [
    "#Loss Plot\n",
    "epochs = range(1 , len(df) + 1)\n",
    "\n",
    "plt.plot(epochs , df['loss'] , 'bo' , label = 'Training Loss')\n",
    "plt.plot(epochs , df['val_loss'] , 'b' , label = 'Validation Loss')\n",
    "plt.legend()\n",
    "plt.title('Training and Validation Loss')\n",
    "plt.xlabel('Number of Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": [
    "#Accuracy Plot\n",
    "epochs = range(1 , len(df) + 1)\n",
    "\n",
    "plt.plot(epochs , df['accuracy'] , 'bo' , label = 'Training Accuracy')\n",
    "plt.plot(epochs , df['val_accuracy'] , 'b' , label = 'Validation Accuracy')\n",
    "plt.legend()\n",
    "plt.title('Training and Validation Accuracy')\n",
    "plt.xlabel('Number of Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our model overfits the data so it would be preferential to stop our model at epoch 4 to get highest accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Building our new model\n",
    "model1 = Sequential()\n",
    "\n",
    "model1.add(Dense(16 , activation = 'relu' , input_shape = (10000 , )))\n",
    "model1.add(Dense(16 , activation = 'relu'))\n",
    "model1.add(Dense(1 , activation = 'sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "model1.compile(loss = 'binary_crossentropy',\n",
    "              optimizer = 'rmsprop',\n",
    "              metrics = ['accuracy']) #Compiling our model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 25000 samples\n",
      "Epoch 1/4\n",
      "25000/25000 [==============================] - 2s 68us/sample - loss: 0.4775 - accuracy: 0.8196\n",
      "Epoch 2/4\n",
      "25000/25000 [==============================] - 1s 49us/sample - loss: 0.2743 - accuracy: 0.9062\n",
      "Epoch 3/4\n",
      "25000/25000 [==============================] - 1s 46us/sample - loss: 0.2092 - accuracy: 0.9262\n",
      "Epoch 4/4\n",
      "25000/25000 [==============================] - 1s 46us/sample - loss: 0.1726 - accuracy: 0.9386\n"
     ]
    }
   ],
   "source": [
    "#training the new model\n",
    "\n",
    "history = model1.fit(X_train,\n",
    "                    y_train,\n",
    "                    epochs = 4,\n",
    "                    batch_size = 512,\n",
    "                    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25000/1 - 3s - loss: 1.8761 - accuracy: 0.4968\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[2.080352459526062, 0.49676]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1.evaluate(X_test , y_test , verbose = 2) #Evaluating our model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are going to keep our model at this only, even though our simple approach didn't do better on our test data. But, to just keep it as a beginner thing we are going to keep it here and do some "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}