# Multilayer perceptron working environment.
# Getting ready the work environment. Importing libraries and modules: 
import time
import pandas as pd
import re
import nltk
import torch 
import torch.nn as nn
import numpy as np
import string
import matplotlib.pyplot as plt
import seaborn as sns

from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.metrics import accuracy_score, precision_recall_fscore_support, classification_report, roc_curve, roc_auc_score, confusion_matrix
from collections import Counter
from bs4 import BeautifulSoup
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize

#===========         Extra tools for the statistic analysis              ======================
from nltk.stem import WordNetLemmatizer

lemmatizer = WordNetLemmatizer()
#----------------------------------------------------------------------

stop_words = stopwords.words('english')
tfidf_vectorizer = TfidfVectorizer(stop_words='english')
vectorizer = CountVectorizer()

#Importing dataset
imdb_path = 'IMDB.csv'
imdb = pd.read_csv(imdb_path)

#Convert sentiment column to binary class
imdb['sentiment'] = imdb['sentiment'].map({'positive': 1, 'negative': 0})

#Checking data and columns
print(imdb.head())

                                              review  sentiment
0  One of the other reviewers has mentioned that ...          1
1  A wonderful little production. <br /><br />The...          1
2  I thought this was a wonderful way to spend ti...          1
3  Basically there's a family where a little boy ...          0
4  Petter Mattei's "Love in the Time of Money" is...          1

#During the applycation of the model in earlier stages, the machine prompted "error" due to the large dataset
#Hence data is cut off to 5000 rows:

#Firstly, let's segregate the sentiment column:
positive_reviews = imdb[imdb['sentiment'] == 1]
negative_reviews = imdb[imdb['sentiment'] == 0]

#Secondly, sampling randomly 2500 reviews from each (+/-)
positive_sample = positive_reviews.sample(n=2500, random_state=42)
negative_sample = negative_reviews.sample(n=2500, random_state=42)

#Putting them together again
imdb_reduced = pd.concat([positive_sample, negative_sample])

#Suffling the new dataset
imdb_reduced = imdb_reduced.sample(frac=1, random_state=42).reset_index(drop=True)


#Sources: 
# https://stackoverflow.com/questions/71758460/effect-of-pandas-dataframe-sample-with-frac-set-to-1
# https://stackoverflow.com/questions/57300260/how-to-drop-added-column-after-using-df-samplefrac-1
# https://docs.python.org/3/library/fractions.html
# https://datascience.stanford.edu/news/splitting-data-randomly-can-ruin-your-model
# https://stats.stackexchange.com/questions/484000/how-to-appropriately-reduce-data-size-or-take-a-representative-sample-from-it

#Function to remove HTML tags:
def remove_html(text):
    soup = BeautifulSoup(text, "html.parser")
    return soup.get_text()

#Sources: 
#https://stackoverflow.com/questions/328356/extracting-text-from-html-file-using-python?newreg=aa9f4dc4aea341cc96661f3b6b26efd6
#https://beautiful-soup-4.readthedocs.io/en/latest/
#https://www.datacamp.com/tutorial/web-scraping-using-python
#https://www.geeksforgeeks.org/how-to-write-the-output-to-html-file-with-python-beautifulsoup/

#Function to remove URLs characteres:
def remove_urls(text):
    return re.sub(r'https?://\S+|www\.\S+', '', text)

#Source: https://www.geeksforgeeks.org/remove-urls-from-string-in-python/

#Function to put together all the previous functions:
def preprocess_1(text):
    text = remove_html(text)
    text = remove_urls(text)
    text = text.lower()
    #text = remove_punctuation(text)///\\\\Initially used a function to remove punctuation, however the outcome was a new massive dataset making impossible to successfully run the whole code.
    return text

#Running the function to make the first preprocessing step.
imdb_reduced['review'] = imdb_reduced['review'].apply(preprocess_1)
imdb['review_preprocess_1'] = imdb['review'].apply(preprocess_1)

C:\Users\nonox\AppData\Local\Temp\ipykernel_16008\3164958809.py:3: MarkupResemblesLocatorWarning: The input looks more like a filename than markup. You may want to open this file and pass the filehandle into Beautiful Soup.
  soup = BeautifulSoup(text, "html.parser")

#Function to tokenize and convert to lower case the text in review column
def tokenize(text):
    tokens = re.findall(r'\b\w+\b', text)
    return tokens

#Tokenization
imdb_reduced['Token'] = imdb_reduced['review'].apply(tokenize)
imdb['Token'] = imdb['review_preprocess_1'].apply(tokenize)

#Function to remove stop words from the tokenized review column
def remove_stopwords(tokens):
    filtered_tokens = [word for word in tokens if word not in stop_words]
    return filtered_tokens

#Remove stopwords
imdb_reduced['Token'] = imdb_reduced['Token'].apply(remove_stopwords)
imdb['Token'] = imdb['Token'].apply(remove_stopwords)

print(imdb.head())

                                              review  sentiment  \
0  One of the other reviewers has mentioned that ...          1   
1  A wonderful little production. <br /><br />The...          1   
2  I thought this was a wonderful way to spend ti...          1   
3  Basically there's a family where a little boy ...          0   
4  Petter Mattei's "Love in the Time of Money" is...          1   

                                 review_preprocess_1  \
0  one of the other reviewers has mentioned that ...   
1  a wonderful little production. the filming tec...   
2  i thought this was a wonderful way to spend ti...   
3  basically there's a family where a little boy ...   
4  petter mattei's "love in the time of money" is...   

                                               Token  
0  [one, reviewers, mentioned, watching, 1, oz, e...  
1  [wonderful, little, production, filming, techn...  
2  [thought, wonderful, way, spend, time, hot, su...  
3  [basically, family, little, boy, jake, thinks,...  
4  [petter, mattei, love, time, money, visually, ...

#Calculating the total tokens for each review
imdb['token_count'] = imdb['Token'].apply(lambda x: len(x) if isinstance(x, list) else 0)

#Dispersion and central tendency measurements
statistics = imdb.groupby('sentiment')['token_count'].agg(['min', 'max', 'mean', 'var', 'std'])

#Avg words per review:
avg_words = imdb['Token'].apply(len).mean()

#Print the statistics
print("Statistics by Sentiment: ")
print('\n')
print(statistics)
print('\n')
print('\n')
print('Average Words: ', f"{avg_words:.0f}")

#Resources:
#https://www.geeksforgeeks.org/pandas-groupby-one-column-and-get-mean-min-and-max-values/
#https://www.kaggle.com/code/akshaysehgal/ultimate-guide-to-pandas-groupby-aggregate

Statistics by Sentiment: 


           min   max       mean          var        std
sentiment                                              
0            3   824  117.81908  7385.840662  85.940914
1            6  1429  121.28256  8905.303412  94.367915


Average Words:  120

#Iterating through the list of lists(each row) to create a new list with all the tokens
def word_freq(list_of_list):
    single_list = [item for sublist in list_of_list for item in sublist]
    token_freq = Counter(single_list)
    return token_freq

#Counting the frequency for each word.
word_frequency = word_freq(imdb['Token'])
print(word_frequency)

#Sources: https://www.datacamp.com/tutorial/pandas-apply

Counter({'movie': 87934, 'film': 79669, 'one': 53585, 'like': 40160, 'good': 29737, 'time': 25099, 'even': 24855, 'would': 24599, 'story': 23108, 'really': 23089, 'see': 23021, 'well': 21259, 'much': 19314, 'bad': 18461, 'get': 18413, 'people': 18181, 'great': 18136, 'also': 17972, 'first': 17576, 'made': 16150, 'make': 15892, 'way': 15644, 'could': 15562, 'movies': 15301, 'characters': 14444, 'think': 14334, 'watch': 13938, 'character': 13900, 'films': 13750, 'two': 13541, 'many': 13443, 'seen': 13371, 'love': 13004, 'never': 12979, 'plot': 12975, 'life': 12911, 'acting': 12857, 'show': 12655, 'best': 12611, 'know': 12508, 'little': 12429, 'ever': 12013, 'man': 11820, 'better': 11426, 'end': 11115, 'scene': 10960, 'still': 10856, 'say': 10767, 'scenes': 10472, 'something': 10142, 'go': 9960, 'back': 9675, 'real': 9424, 'thing': 9168, 'watching': 9160, 'actors': 8948, 'director': 8804, 'years': 8757, 'funny': 8755, 'though': 8750, 'old': 8650, 'another': 8584, 'work': 8544, '10': 8473, 'actually': 8466, 'nothing': 8371, 'makes': 8307, 'look': 8293, 'find': 8218, 'going': 8189, 'new': 8092, 'lot': 8083, 'every': 7964, 'part': 7873, 'world': 7495, 'cast': 7422, 'us': 7373, 'things': 7335, 'want': 7301, 'quite': 7274, 'pretty': 7250, 'horror': 7248, 'around': 7151, 'seems': 7074, 'young': 7042, 'take': 6983, 'big': 6958, 'however': 6958, 'got': 6945, 'thought': 6934, 'fact': 6901, 'enough': 6892, 'long': 6880, 'give': 6702, 'may': 6614, 'comedy': 6573, 'series': 6555, 'must': 6543, 'right': 6528, 'action': 6495, 'music': 6465, 'without': 6437, 'guy': 6389, 'times': 6354, 'saw': 6340, 'original': 6331, 'always': 6326, 'come': 6290, 'role': 6290, 'almost': 6257, 'gets': 6235, 'point': 6178, 'interesting': 6178, 'done': 6153, 'whole': 6120, 'least': 6102, 'far': 5992, 'bit': 5965, 'script': 5927, 'family': 5882, 'minutes': 5866, 'feel': 5834, '2': 5806, 'might': 5793, 'making': 5776, 'anything': 5765, 'since': 5740, 'tv': 5700, 'last': 5699, 'probably': 5619, 'performance': 5572, 'kind': 5547, 'away': 5508, 'girl': 5494, 'yet': 5478, 'fun': 5391, 'anyone': 5373, 'woman': 5339, 'worst': 5332, 'sure': 5293, 'rather': 5280, 'hard': 5271, 'day': 5254, 'played': 5186, 'found': 5160, 'looking': 5052, 'screen': 5039, 'although': 5010, 'especially': 5002, 'believe': 4991, 'dvd': 4931, 'trying': 4906, 'course': 4892, 'everything': 4822, 'set': 4808, 'goes': 4758, 'book': 4757, 'ending': 4752, 'comes': 4750, 'put': 4748, 'maybe': 4737, 'place': 4724, 'shows': 4705, 'let': 4703, 'three': 4699, 'worth': 4672, 'different': 4665, 'actor': 4644, 'main': 4608, 'someone': 4600, 'sense': 4599, 'american': 4578, 'reason': 4538, 'play': 4531, 'effects': 4529, 'looks': 4524, 'true': 4518, 'money': 4509, 'watched': 4501, 'everyone': 4484, 'year': 4483, 'job': 4460, 'together': 4450, 'war': 4446, 'high': 4341, 'plays': 4332, 'audience': 4317, 'instead': 4314, 'half': 4307, 'said': 4306, 'later': 4278, '1': 4272, 'takes': 4265, 'special': 4242, 'john': 4236, 'seem': 4229, 'night': 4228, 'beautiful': 4218, 'left': 4194, 'black': 4186, 'seeing': 4171, 'wife': 4117, 'version': 4110, 'shot': 4105, 'excellent': 4098, 'idea': 4072, 'house': 4072, 'star': 4026, 'else': 3990, 'mind': 3985, 'death': 3943, 'fan': 3920, 'father': 3894, 'used': 3871, 'nice': 3863, 'budget': 3860, 'simply': 3858, 'poor': 3842, 'short': 3840, 'completely': 3833, '3': 3831, 'second': 3828, 'men': 3812, 'read': 3791, 'less': 3763, 'along': 3758, 'top': 3750, 'dead': 3704, 'home': 3703, 'help': 3687, 'hollywood': 3683, 'line': 3679, 'either': 3671, 'kids': 3663, 'boring': 3639, 'friends': 3634, 'camera': 3597, 'production': 3594, 'try': 3590, 'wrong': 3582, 'low': 3580, 'enjoy': 3580, 'classic': 3577, 'use': 3576, 'given': 3568, 'women': 3561, 'need': 3552, 'full': 3551, 'school': 3535, 'stupid': 3530, 'next': 3503, 'performances': 3495, 'rest': 3476, 'truly': 3466, 'couple': 3465, 'video': 3459, 'awful': 3444, 'sex': 3420, 'start': 3411, 'recommend': 3401, 'tell': 3377, 'terrible': 3332, 'mean': 3326, 'remember': 3319, 'getting': 3313, 'came': 3309, 'understand': 3299, 'perhaps': 3290, 'name': 3273, 'moments': 3266, 'face': 3243, 'keep': 3235, 'person': 3234, 'human': 3220, 'wonderful': 3215, 'mother': 3211, 'playing': 3199, 'style': 3196, 'episode': 3183, 'small': 3180, 'others': 3173, 'boy': 3157, 'perfect': 3153, 'early': 3132, 'stars': 3129, 'head': 3124, 'often': 3124, 'written': 3111, 'definitely': 3110, 'lines': 3090, 'children': 3087, 'dialogue': 3073, 'gives': 3069, 'piece': 3046, 'went': 3009, 'case': 3003, 'finally': 3001, 'yes': 3000, 'absolutely': 2994, 'live': 2993, 'laugh': 2980, 'title': 2976, 'oh': 2973, 'friend': 2968, 'certainly': 2968, 'lost': 2947, 'liked': 2943, 'become': 2940, 'entertaining': 2938, 'worse': 2938, 'sort': 2937, 'cinema': 2932, 'loved': 2925, 'picture': 2923, 'called': 2914, 'hope': 2912, 'felt': 2875, 'mr': 2873, 'guys': 2866, 'based': 2860, 'entire': 2857, 'supposed': 2856, 'several': 2856, 'overall': 2856, 'drama': 2829, 'sound': 2812, 'problem': 2810, 'white': 2806, 'waste': 2785, '4': 2784, '5': 2778, 'beginning': 2776, 'dark': 2770, 'fans': 2759, 'game': 2746, 'totally': 2744, 'care': 2737, 'humor': 2728, 'wanted': 2716, 'direction': 2705, 'seemed': 2703, 'evil': 2699, 'lives': 2697, 'lead': 2695, 'despite': 2693, 'guess': 2681, 'final': 2678, 'example': 2678, 'turn': 2675, 'already': 2675, 'throughout': 2665, 'becomes': 2648, 'killer': 2638, 'able': 2623, 'unfortunately': 2622, 'son': 2615, 'quality': 2606, 'days': 2590, 'history': 2589, 'b': 2566, 'heart': 2564, 'side': 2554, 'fine': 2545, 'michael': 2539, 'flick': 2533, 'wants': 2529, 'writing': 2525, 'horrible': 2524, 'amazing': 2519, 'run': 2502, 'today': 2498, 'art': 2491, 'town': 2488, 'act': 2478, 'close': 2470, 'works': 2469, 'kill': 2468, 'god': 2465, 'child': 2460, 'matter': 2446, 'etc': 2443, 'viewer': 2428, 'tries': 2428, 'past': 2423, 'genre': 2418, 'enjoyed': 2415, 'turns': 2415, 'brilliant': 2411, 'behind': 2406, 'gave': 2406, 'car': 2402, 'stuff': 2400, 'parts': 2399, 'eyes': 2398, 'favorite': 2389, 'girls': 2383, 'directed': 2382, 'hand': 2381, 'kid': 2378, 'late': 2374, 'city': 2368, 'expect': 2354, 'hour': 2350, 'soon': 2345, 'actress': 2329, 'obviously': 2326, 'sometimes': 2323, 'thinking': 2309, 'killed': 2308, 'starts': 2298, 'stop': 2284, 'decent': 2284, 'type': 2281, 'self': 2280, 'daughter': 2279, 'highly': 2276, 'group': 2271, 'says': 2258, 'blood': 2256, 'voice': 2250, 'anyway': 2243, 'writer': 2235, 'known': 2230, 'took': 2228, 'heard': 2218, 'happens': 2215, 'except': 2213, 'fight': 2212, 'feeling': 2205, 'experience': 2201, 'coming': 2193, 'slow': 2186, 'moment': 2159, 'stories': 2149, 'leave': 2145, 'police': 2137, 'told': 2135, 'extremely': 2130, 'score': 2130, 'violence': 2122, 'hero': 2122, 'involved': 2119, 'chance': 2112, 'strong': 2112, 'lack': 2109, 'hit': 2104, 'ok': 2103, 'hilarious': 2101, 'cannot': 2099, 'happen': 2093, 'wonder': 2093, 'roles': 2091, 'particularly': 2088, 'living': 2088, 'including': 2086, 'hell': 2084, 'save': 2083, 'brother': 2080, 'murder': 2079, 'crap': 2078, 'looked': 2075, 'cool': 2074, 'david': 2071, 'simple': 2067, 'please': 2064, 'age': 2064, 'cut': 2063, 'obvious': 2056, 'complete': 2055, 'happened': 2054, 'song': 2054, 'serious': 2052, 'gore': 2049, 'james': 2046, 'attempt': 2039, 'ago': 2036, 'taken': 2029, 'shown': 2029, 'robert': 2025, 'english': 2021, 'husband': 2019, 'reality': 2010, 'seriously': 2005, 'released': 2002, 'jokes': 1992, 'opening': 1987, 'interest': 1986, 'alone': 1979, 'none': 1979, 'across': 1979, 'sad': 1966, 'exactly': 1964, 'possible': 1964, 'career': 1958, 'number': 1954, 'saying': 1953, 'hours': 1949, 'usually': 1947, 'talent': 1943, 'cinematography': 1942, 'view': 1937, 'documentary': 1937, 'relationship': 1931, 'annoying': 1929, 'running': 1929, 'order': 1925, 'wish': 1925, 'huge': 1916, 'whose': 1905, 'shots': 1904, 'body': 1898, 'light': 1895, 'taking': 1894, 'country': 1892, 'ridiculous': 1891, 'power': 1890, 'middle': 1884, 'call': 1883, 'important': 1883, 'level': 1880, 'ends': 1879, 'female': 1878, 'started': 1874, 'major': 1874, 'four': 1872, 'word': 1868, 'turned': 1862, 'jack': 1862, 'opinion': 1861, 'change': 1856, 'scary': 1856, 'mostly': 1855, 'usual': 1850, 'happy': 1847, 'room': 1847, 'beyond': 1846, 'ones': 1846, 'silly': 1843, 'rating': 1834, 'somewhat': 1833, 'words': 1832, 'novel': 1832, 'knows': 1826, 'talking': 1826, 'knew': 1824, 'disappointed': 1817, 'strange': 1809, 'apparently': 1807, 'non': 1807, 'upon': 1800, 'attention': 1800, 'modern': 1799, 'single': 1793, 'finds': 1791, 'basically': 1790, 'cheap': 1790, 'television': 1789, 'musical': 1784, 'due': 1783, 'earth': 1774, 'problems': 1771, 'miss': 1767, 'episodes': 1755, 'clearly': 1748, 'local': 1746, 'thriller': 1744, 'king': 1739, '7': 1738, 'five': 1730, 'british': 1728, 'talk': 1727, 'class': 1723, 'events': 1722, 'sequence': 1720, 'team': 1712, 'french': 1710, 'moving': 1700, 'ten': 1697, 'fast': 1694, 'straight': 1690, 'tells': 1689, 'review': 1689, 'predictable': 1687, 'comic': 1686, 'die': 1685, '8': 1684, 'entertainment': 1684, 'songs': 1679, 'space': 1678, 'whether': 1675, 'george': 1671, 'future': 1671, 'dialog': 1668, 'add': 1667, 'sets': 1656, 'easily': 1653, 'enjoyable': 1652, 'near': 1652, 'appears': 1650, 'soundtrack': 1646, 'hate': 1645, 'bring': 1639, 'giving': 1638, 'lots': 1638, 'romantic': 1637, 'similar': 1632, 'supporting': 1629, 'message': 1626, 'release': 1624, 'mention': 1622, 'within': 1620, 'eye': 1617, 'sequel': 1617, 'filmed': 1617, 'falls': 1613, 'sister': 1611, 'rock': 1611, 'monster': 1609, 'clear': 1608, 'needs': 1605, 'dull': 1603, 'bunch': 1602, 'suspense': 1600, 'surprised': 1594, 'showing': 1593, 'easy': 1592, 'sorry': 1591, 'tried': 1589, 'certain': 1587, 'working': 1586, 'ways': 1585, 'theme': 1579, 'theater': 1577, 'parents': 1573, 'among': 1567, 'storyline': 1563, 'named': 1563, 'lady': 1557, 'stay': 1556, 'fall': 1555, 'effort': 1554, 'stand': 1552, 'minute': 1551, 'gone': 1549, 'feature': 1548, '9': 1543, 'tale': 1540, 'dr': 1540, 'dog': 1540, 'buy': 1539, 'using': 1538, 'comments': 1535, 'typical': 1535, 'mystery': 1533, 'editing': 1528, 'subject': 1527, 'deal': 1526, 'avoid': 1525, 'oscar': 1525, 'doubt': 1523, 'richard': 1522, 'peter': 1520, 'fantastic': 1518, 'feels': 1516, 'kept': 1515, 'nearly': 1515, 'realistic': 1515, 'viewers': 1515, 'general': 1513, 'viewing': 1510, 'elements': 1509, 'okay': 1507, 'points': 1506, 'greatest': 1503, 'check': 1503, 'means': 1502, 'famous': 1499, 'crime': 1498, 'imagine': 1495, 'rent': 1494, 'paul': 1492, 'red': 1487, 'actual': 1486, 'form': 1486, 'follow': 1478, 'period': 1470, 'material': 1469, 'believable': 1468, '20': 1466, 'move': 1463, 'brought': 1462, 'tom': 1462, 'somehow': 1456, 'forget': 1454, 'begins': 1453, 'animation': 1445, 'leads': 1444, 'doctor': 1441, 'reviews': 1441, 'surprise': 1435, 'figure': 1434, 'america': 1431, 'weak': 1430, 'hear': 1428, 'sit': 1426, 'average': 1424, 'open': 1422, 'york': 1419, 'deep': 1419, 'sequences': 1417, 'fi': 1416, 'killing': 1414, 'atmosphere': 1413, 'wait': 1411, 'eventually': 1409, 'premise': 1409, 'learn': 1408, 'sci': 1408, 'imdb': 1407, 'whatever': 1403, 'expected': 1400, 'dance': 1397, 'lame': 1393, 'indeed': 1393, 'boys': 1393, 'particular': 1391, 'poorly': 1390, 'note': 1389, 'box': 1386, 'truth': 1384, 'situation': 1384, 'hot': 1383, 'shame': 1381, 'third': 1379, 'free': 1373, 'society': 1373, 'season': 1371, 'decided': 1370, 'difficult': 1360, 'romance': 1359, 'lee': 1359, 'needed': 1356, 'crew': 1351, 'acted': 1349, 'gay': 1348, 'leaves': 1348, 'emotional': 1345, 'unless': 1345, 'western': 1345, 'possibly': 1344, 'footage': 1343, 'sexual': 1340, 'credits': 1336, 'write': 1336, 'de': 1336, 'forced': 1333, 'became': 1333, 'memorable': 1332, 'reading': 1332, 'begin': 1331, 'air': 1330, 'meet': 1328, 'otherwise': 1327, 'question': 1326, 'male': 1325, 'street': 1318, 'e': 1315, 'nature': 1313, 'meets': 1309, 'beauty': 1308, 'plus': 1307, 'cheesy': 1306, 'hands': 1306, 'screenplay': 1301, 'masterpiece': 1299, 'superb': 1299, 'effect': 1297, 'features': 1297, 'stage': 1295, 'interested': 1292, 'island': 1291, 'perfectly': 1291, 'laughs': 1291, 'forward': 1287, 'comment': 1287, 'cop': 1286, 'weird': 1284, 'badly': 1282, 'previous': 1281, 'inside': 1281, 'sounds': 1280, 'japanese': 1279, 'personal': 1278, 'quickly': 1272, '80': 1269, 'crazy': 1268, 'total': 1267, 'baby': 1266, 'mark': 1263, 'keeps': 1262, 'result': 1262, 'towards': 1261, 'girlfriend': 1261, 'disney': 1261, 'battle': 1259, 'writers': 1259, 'fire': 1253, 'setting': 1253, 'joe': 1252, 'worked': 1250, 'earlier': 1249, 'background': 1249, 'incredibly': 1249, 'mess': 1247, 'copy': 1240, 'dumb': 1239, 'unique': 1238, 'bill': 1238, 'business': 1237, 'realize': 1236, 'directors': 1236, 'powerful': 1234, 'water': 1233, 'dramatic': 1233, 'older': 1231, 'rate': 1230, 'dream': 1227, 'joke': 1227, 'pay': 1224, 'following': 1223, 'plenty': 1219, 'directing': 1219, 'ask': 1218, 'creepy': 1216, 'various': 1215, 'development': 1214, 'appear': 1212, 'front': 1212, 'brings': 1211, 'rich': 1203, '30': 1195, 'admit': 1195, 'political': 1194, 'apart': 1194, 'cover': 1194, 'leading': 1192, 'fairly': 1192, 'reasons': 1191, 'portrayed': 1190, 'spent': 1189, 'era': 1185, 'gun': 1183, 'present': 1180, 'telling': 1179, 'party': 1179, 'outside': 1178, 'fighting': 1176, 'wasted': 1176, 'zombie': 1175, 'william': 1172, 'fails': 1170, 'deserves': 1170, 'success': 1167, 'break': 1164, 'twist': 1163, 'secret': 1160, 'meant': 1159, 'list': 1156, 'return': 1153, 'expecting': 1151, 'create': 1151, 'attempts': 1150, 'missing': 1150, 'ideas': 1150, 'ended': 1147, 'manages': 1146, 'recently': 1146, 'agree': 1145, 'cute': 1145, 'villain': 1144, 'german': 1143, 'fantasy': 1142, 'talented': 1140, 'c': 1140, 'caught': 1140, 'odd': 1138, 'large': 1138, 'unlike': 1137, 'hardly': 1134, 'produced': 1134, 'match': 1132, 'clever': 1130, 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775, 'worthy': 772, 'hospital': 772, 'image': 771, 'million': 770, 'double': 770, 'stunning': 769, 'tired': 769, 'justice': 769, 'sent': 769, 'cry': 767, 'drawn': 764, 'pain': 763, 'fascinating': 762, 'six': 762, 'born': 762, 'stewart': 761, 'charm': 760, 'support': 758, 'lover': 758, 'holes': 757, 'opportunity': 757, 'martin': 755, 'fresh': 754, 'ahead': 754, 'laughed': 753, 'christopher': 752, 'cliché': 752, 'color': 752, 'hitchcock': 751, 'offer': 751, 'faces': 750, 'confusing': 749, 'students': 749, 'dreams': 749, 'shock': 748, 'finding': 748, 'random': 747, 'news': 745, 'batman': 744, 'direct': 743, 'location': 743, 'jean': 743, 'happening': 742, 'agent': 742, 'flicks': 742, 'confused': 741, 'include': 739, 'content': 739, 'billy': 739, 'summer': 738, 'suggest': 738, 'drugs': 736, 'exception': 735, 'folks': 735, 'held': 734, 'marriage': 734, 'porn': 734, 'kelly': 733, 'mom': 732, 'beat': 732, 'bar': 732, 'twice': 731, 'dies': 730, 'noir': 730, 'answer': 730, 'likable': 728, 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'favor': 511, 'fate': 510, 'talents': 510, 'insane': 510, 'dimensional': 509, 'sign': 509, 'andy': 509, 'saved': 508, 'seat': 508, 'received': 507, 'skills': 506, 'program': 506, 'remembered': 506, 'sheer': 506, 'attitude': 505, 'african': 505, 'sucked': 504, 'study': 504, 'parody': 503, 'fly': 503, 'genuinely': 502, 'context': 502, 'unfunny': 502, 'luck': 501, 'dated': 501, 'levels': 500, 'miles': 500, 'cash': 500, 'range': 500, 'ann': 500, 'aliens': 500, 'visit': 499, 'eric': 499, 'vote': 498, 'jimmy': 498, 'reaction': 498, 'site': 497, 'screaming': 497, 'travel': 497, 'utter': 497, 'visuals': 496, 'portray': 496, 'spectacular': 496, 'satire': 495, 'v': 495, 'failure': 495, 'julie': 495, 'gangster': 494, 'lesson': 494, 'reviewers': 493, 'grown': 493, 'decade': 493, 'classics': 493, 'gory': 493, 'dreadful': 493, 'irritating': 493, 'unable': 493, 'football': 492, 'evidence': 492, 'reach': 492, 'debut': 492, 'flesh': 492, 'existence': 491, 'steal': 491, 'ladies': 491, 'continues': 491, 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'1st': 260, 'dragged': 260, 'typically': 260, 'cinderella': 260, 'curiosity': 259, 'forgive': 259, 'highlights': 259, 'trite': 259, 'cameos': 259, 'shoes': 259, 'territory': 259, 'performing': 258, 'burning': 258, 'inspiring': 258, 'alexander': 258, 'precious': 258, 'loser': 258, 'endearing': 258, 'catholic': 258, 'inevitable': 258, 'fortune': 258, 'admire': 258, 'stood': 257, 'easier': 257, 'bush': 257, 'editor': 257, 'restaurant': 257, 'charisma': 257, 'subplot': 257, 'carl': 257, 'neighborhood': 257, 'poster': 257, 'raped': 257, 'shortly': 256, 'chain': 256, 'eerie': 256, 'creators': 256, 'possessed': 256, 'remaining': 256, 'kudos': 256, 'response': 256, 'audio': 255, 'medium': 255, 'appalling': 255, 'individuals': 255, 'beating': 255, 'horrendous': 255, 'neighbor': 255, 'cabin': 255, 'intention': 254, 'investigation': 254, 'horrors': 254, 'critic': 254, 'repetitive': 254, 'gain': 254, 'colour': 254, 'masters': 254, 'commercials': 254, 'duty': 254, 'accused': 254, 'kubrick': 254, 'punk': 254, 'unintentionally': 253, 'distant': 253, 'corpse': 253, 'breasts': 253, 'admittedly': 253, 'wicked': 253, 'tall': 253, 'pointed': 253, 'pie': 253, 'smoke': 253, 'stanley': 253, 'cringe': 252, 'killings': 252, 'requires': 252, 'handful': 252, 'fifteen': 252, 'lawrence': 252, 'attracted': 252, 'burn': 252, 'brazil': 252, 'heat': 252, 'stole': 252, 'fitting': 252, 'minimal': 252, 'bruno': 252, 'noise': 252, 'commit': 252, 'wallace': 252, 'factory': 251, 'sons': 251, 'tremendous': 251, 'pulling': 251, 'motivation': 251, 'travels': 251, 'stated': 251, 'freak': 251, 'austen': 251, 'nazis': 251, 'threw': 251, 'staff': 251, 'virus': 251, 'loosely': 251, 'rid': 250, 'altogether': 250, 'clues': 250, 'doll': 250, 'tons': 250, 'chicago': 250, 'bride': 250, 'battles': 250, 'watches': 250, 'doors': 250, 'amanda': 250, 'explosions': 250, 'cole': 250, 'noble': 249, 'thumbs': 249, 'oscars': 249, 'engaged': 249, 'dig': 249, 'backdrop': 249, '1980s': 249, 'luckily': 249, 'stiff': 249, 'shirt': 249, 'ho': 249, 'reasonable': 249, 'reactions': 249, 'argue': 249, 'spirited': 248, 'trade': 248, 'hamlet': 248, 'goal': 248, 'spoiled': 248, 'claire': 248, 'threat': 248, 'poverty': 248, 'wonders': 247, 'angeles': 247, 'intent': 247, 'broadcast': 247, 'brilliance': 246, 'horrid': 246, 'wrestling': 246, 'characterization': 246, 'beaten': 246, 'cook': 246, 'russia': 246, 'demands': 246, 'hire': 246, 'occurs': 246, 'allowing': 246, 'mansion': 246, 'thrills': 245, 'eve': 245, 'incoherent': 245, 'photographed': 245, 'cried': 245, 'ironically': 245, '99': 245, 'glenn': 245, 'huh': 245, 'documentaries': 245, 'countless': 245, 'rick': 245, 'resolution': 244, 'press': 244, 'virgin': 244, 'farce': 244, 'plague': 244, 'superbly': 244, 'realise': 244, 'vague': 244, 'providing': 244, 'madonna': 244, 'fx': 243, 'associated': 243, 'interaction': 243, 'believing': 243, 'notes': 243, 'load': 243, 'fancy': 243, 'baker': 243, 'ward': 243, 'jeremy': 243, 'carol': 242, 'owen': 242, 'mild': 242, 'picking': 242, 'dorothy': 242, 'elderly': 242, 'wreck': 242, 'evident': 242, 'merit': 242, 'iii': 242, 'worlds': 242, 'pink': 242, 'tortured': 242, 'hysterical': 242, 'spiritual': 242, 'ken': 242, 'heroic': 242, 'surrounded': 242, 'symbolism': 242, 'idiots': 242, 'areas': 241, 'daring': 241, 'sentence': 241, 'spooky': 241, 'messages': 241, 'matthew': 241, 'shy': 241, 'buried': 241, 'persona': 241, 'definite': 241, 'penn': 241, 'detailed': 241, 'enters': 240, 'conspiracy': 240, 'andrew': 240, 'savage': 240, 'celluloid': 240, 'cheek': 240, 'subsequent': 240, 'spare': 240, 'ranks': 240, 'knock': 240, 'charismatic': 240, 'pleased': 240, 'lab': 240, 'disc': 240, 'reasonably': 240, 'titanic': 240, 'canada': 239, 'hopper': 239, 'accomplished': 239, 'q': 239, 'displays': 239, 'lou': 239, 'waters': 239, 'digital': 239, 'frustrated': 239, 'ah': 239, 'insulting': 239, 'aimed': 239, 'assassin': 239, 'anyways': 239, 'duke': 239, 'swedish': 239, 'ireland': 239, 'strike': 238, 'returned': 238, '2004': 238, 'terrorist': 238, 'fears': 238, 'walls': 238, 'hammer': 238, 'survival': 238, 'neil': 238, 'bell': 238, 'afterwards': 237, 'trail': 237, 'scientific': 237, 'segments': 237, 'extended': 237, 'blowing': 237, 'willis': 237, 'landscape': 237, 'arrive': 237, 'escaped': 237, 'flawless': 237, 'winter': 237, 'scripted': 237, 'presumably': 237, 'arrested': 237, 'parent': 237, 'tunes': 237, 'franco': 237, 'outcome': 237, 'jazz': 237, '1960': 237, 'concerning': 236, 'currently': 236, 'craig': 236, 'beats': 236, 'chances': 236, 'prevent': 236, 'directorial': 236, 'distance': 236, 'worker': 236, 'murray': 236, 'titled': 236, 'gift': 236, 'terrifying': 236, 'abilities': 236, 'finger': 236, 'causing': 236, 'strikes': 236, 'walken': 236, 'cia': 236, 'hanks': 236, 'generous': 236, 'secretary': 236, 'funeral': 236, 'prisoners': 235, 'wondered': 235, 'pushed': 235, 'builds': 235, 'antics': 235, 'disease': 235, 'carrey': 235, 'achievement': 235, 'crack': 235, 'contrary': 235, 'ian': 235, 'sadistic': 235, 'represents': 235, 'occur': 235, 'cared': 235, 'angela': 235, 'frankie': 235, 'timeless': 235, 'narrator': 235, 'che': 235, 'continued': 234, 'liking': 234, 'root': 234, 'citizen': 234, 'web': 234, 'hatred': 234, 'depression': 234, 'painting': 234, 'randy': 234, 'prostitute': 234, 'goodness': 234, 'swear': 234, 'ego': 234, 'jealous': 234, 'pleasantly': 234, 'receive': 234, 'sullivan': 234, 'fonda': 234, 'rocks': 234, 'valley': 234, 'nearby': 233, 'attached': 233, 'kitchen': 233, 'tea': 233, 'secrets': 233, 'threatening': 233, 'burned': 233, 'fighter': 233, 'brando': 233, 'chaos': 233, 'staged': 233, 'scarlett': 233, 'connect': 232, 'complaint': 232, 'stronger': 232, 'tracy': 232, 'discussion': 232, 'sole': 232, 'worthless': 232, 'ya': 232, 'cruise': 232, 'dialogues': 232, 'broad': 232, 'projects': 232, 'strictly': 232, 'worried': 232, 'subjects': 232, 'bears': 232, 'resembles': 232, 'planning': 231, 'combat': 231, 'officers': 231, 'stooges': 231, 'ned': 231, 'mile': 231, 'logical': 231, 'menacing': 231, 'carrie': 231, 'university': 231, 'colonel': 231, 'twin': 231, 'politically': 230, 'fired': 230, 'liberal': 230, 'porno': 230, 'sticks': 230, 'roman': 230, 'z': 230, 'rabbit': 230, 'argument': 230, 'bleak': 230, 'streep': 230, 'rural': 230, 'conceived': 230, 'hardcore': 229, '1990': 229, 'pseudo': 229, 'yellow': 229, 'listed': 229, 'spike': 229, 'ridiculously': 229, 'comics': 229, 'traveling': 229, 'spell': 229, 'altman': 229, 'possibility': 229, 'signs': 229, 'drivel': 229, 'obscure': 229, 'emperor': 229, 'craft': 229, 'disjointed': 229, 'cup': 229, 'comfortable': 228, 'juvenile': 228, 'affected': 228, 'explicit': 228, '1999': 228, 'warrior': 228, 'careers': 228, 'airport': 228, 'racial': 228, 'covers': 228, 'recorded': 228, 'lyrics': 228, 'executive': 228, 'gradually': 227, 'offended': 227, 'empire': 227, 'airplane': 227, 'furthermore': 227, 'campbell': 227, 'brains': 227, 'kidding': 227, 'delivering': 227, 'translation': 227, 'involvement': 227, 'morality': 227, 'todd': 227, 'dinosaurs': 226, 'implausible': 226, 'notably': 226, '2002': 226, 'grandmother': 226, 'unpleasant': 226, 'eccentric': 226, 'conversations': 226, 'meaningless': 225, 'draws': 225, 'mario': 225, 'offering': 225, 'bathroom': 225, 'sadness': 225, 'devoid': 225, 'pants': 225, 'blues': 225, 'argento': 225, 'investigate': 224, 'dire': 224, 'terry': 224, 'popcorn': 224, 'overlooked': 224, 'throat': 224, 'cusack': 224, 'boot': 224, 'trio': 224, 'sacrifice': 223, 'ticket': 223, 'explore': 223, 'improved': 223, 'unwatchable': 223, 'tie': 223, 'influenced': 223, 'eva': 223, 'generic': 223, 'prisoner': 223, 'lincoln': 223, 'cats': 223, 'rex': 222, 'vast': 222, 'isolated': 222, 'davies': 222, 'stinker': 222, 'installment': 222, 'synopsis': 222, 'tracks': 222, 'bates': 222, 'drunken': 222, 'groups': 222, 'derek': 222, 'differences': 222, 'exaggerated': 222, 'primarily': 222, 'screams': 222, 'sandler': 222, 'rita': 222, 'staying': 221, 'shining': 221, 'estate': 221, 'judging': 221, 'philosophy': 221, 'bull': 221, 'gentle': 221, 'removed': 221, 'holy': 221, 'bin': 221, 'agents': 221, 'unbelievably': 221, 'museum': 221, 'specifically': 221, 'aforementioned': 221, 'wax': 221, 'thrill': 220, 'slightest': 220, 'reached': 220, 'martha': 220, 'planned': 220, 'quinn': 220, 'blank': 220, 'blend': 220, 'secondly': 220, 'dedicated': 220, 'errors': 220, 'scarecrow': 220, 'duo': 220, 'rendition': 219, 'odds': 219, 'closed': 219, 'harder': 219, 'psychotic': 219, 'newspaper': 219, 'marks': 219, 'limits': 219, 'justin': 219, 'reed': 219, 'amy': 219, 'models': 219, 'buster': 219, 'proceedings': 219, 'hitch': 219, 'branagh': 219, 'wannabe': 219, 'manhattan': 219, 'sidekick': 218, 'rage': 218, 'benefit': 218, 'bullets': 218, 'sport': 218, 'conventional': 218, 'sloppy': 218, 'thick': 218, 'pretend': 218, 'recognition': 217, 'unbearable': 217, 'bullet': 217, 'iron': 217, 'movements': 217, 'yesterday': 217, 'disturbed': 217, 'explosion': 217, 'misses': 217, 'orders': 217, '1930': 217, 'relative': 217, 'ideal': 217, 'doomed': 217, '2007': 217, 'murderous': 216, 'constructed': 216, 'explaining': 216, '2003': 216, 'decisions': 216, 'dignity': 216, 'eighties': 216, 'tag': 216, 'subtlety': 216, 'devoted': 216, 'diamond': 216, 'opinions': 216, 'coffee': 216, 'hook': 216, 'poetry': 216, 'abc': 216, 'fetched': 215, 'aging': 215, 'highest': 215, 'robbery': 215, 'beneath': 215, 'overdone': 215, 'producing': 215, 'wont': 215, 'garden': 215, 'genres': 215, 'buddies': 215, 'vicious': 215, 'photographer': 215, 'intrigued': 215, 'folk': 215, '2nd': 215, 'measure': 215, 'elaborate': 215, 'occurred': 215, 'mafia': 214, 'slap': 214, 'giallo': 214, 'temple': 214, 'intrigue': 214, 'block': 214, 'trained': 214, 'absence': 214, 'kurt': 214, 'preview': 214, 'stereotype': 214, 'rank': 214, 'mixture': 214, 'motives': 214, '50s': 214, 'cards': 214, 'earned': 214, 'ranger': 214, 'similarly': 213, 'exposed': 213, 'turner': 213, 'kirk': 213, 'imagined': 213, 'blake': 213, 'crystal': 213, 'fatal': 213, 'wet': 213, 'complain': 213, 'lucas': 213, 'partly': 213, 'bette': 212, 'pushing': 212, 'clumsy': 212, 'fields': 212, 'classical': 212, 'contained': 212, 'lit': 212, 'winds': 212, 'damage': 212, 'clothing': 212, 'recognized': 212, 'arrogant': 212, 'cliff': 212, 'suited': 212, 'boll': 211, 'francis': 211, 'alfred': 211, 'clown': 211, 'population': 211, 'leg': 211, 'wine': 211, 'claude': 211, 'lifestyle': 211, 'commented': 211, 'lol': 211, 'shine': 211, 'official': 211, 'grab': 211, 'unintentional': 210, 'nicole': 210, 'keith': 210, 'convoluted': 210, 'relies': 210, 'homosexual': 210, 'leonard': 210, 'illegal': 210, 'performer': 210, 'reduced': 210, 'existed': 210, 'inventive': 210, 'enormous': 210, 'damme': 210, 'mate': 210, 'reunion': 210, 'plant': 210, 'darker': 209, 'simplistic': 209, 'squad': 209, 'junior': 209, 'border': 209, 'ruthless': 209, 'represent': 209, 'dust': 209, 'montage': 209, 'selfish': 209, 'captivating': 209, 'principal': 209, 'lips': 209, 'pays': 209, 'ruins': 208, '00': 208, 'blatant': 208, 'versus': 208, 'doc': 208, 'cameras': 208, 'chest': 208, 'ought': 208, 'undoubtedly': 208, 'superficial': 208, 'afford': 208, 'agreed': 208, 'satisfied': 208, 'fingers': 208, 'rubber': 208, 'bourne': 208, 'menace': 207, 'reaches': 207, 'enemies': 207, 'depressed': 207, 'strangers': 207, 'eaten': 207, 'armed': 207, 'passes': 207, 'vegas': 207, 'childish': 207, 'distracting': 207, 'ruth': 207, 'innovative': 206, 'mistaken': 206, 'bug': 206, 'primary': 206, 'choreography': 206, 'user': 206, 'buff': 206, 'cd': 206, 'failing': 206, 'centered': 206, 'education': 206, 'composed': 206, 'eastern': 206, 'da': 206, 'solo': 206, 'unfolds': 205, 'foul': 205, 'twelve': 205, 'terrorists': 205, 'solely': 205, 'curtis': 205, 'honesty': 205, 'splendid': 205, 'painted': 204, 'sheen': 204, '90s': 204, 'describes': 204, 'method': 204, 'circle': 204, 'adequate': 204, 'introduces': 204, 'miserable': 204, 'involve': 204, 'hamilton': 204, 'loads': 204, 'writes': 204, 'chicken': 204, 'myers': 204, 'succeeded': 204, 'macy': 204, 'psychiatrist': 204, 'smooth': 204, 'glover': 204, 'copies': 204, 'progress': 204, 'buildings': 204, 'catches': 204, 'depicts': 204, 'styles': 204, 'trees': 203, 'revolutionary': 203, 'mtv': 203, 'lay': 203, 'seventies': 203, 'margaret': 203, 'bottle': 203, 'immensely': 203, 'smaller': 203, 'coherent': 203, 'et': 203, 'scheme': 203, 'stellar': 203, 'instant': 203, 'flies': 203, 'comparing': 203, 'nightmares': 203, 'hollow': 203, 'parallel': 202, 'tame': 202, 'accompanied': 202, 'calm': 202, 'props': 202, 'alternative': 202, 'contract': 202, 'punishment': 202, 'consistently': 202, 'annie': 202, 'witnessed': 202, 'countryside': 202, 'closet': 201, 'shirley': 201, 'wrapped': 201, 'rooms': 201, 'teaching': 201, '1940': 201, 'report': 201, 'aid': 201, 'cox': 201, 'belong': 201, 'cheating': 201, 'notion': 201, 'shelf': 201, 'explored': 201, 'slick': 201, 'fix': 201, 'loyal': 201, 'poetic': 201, 'companion': 201, 'rap': 201, 'jet': 201, 'spring': 201, 'purchase': 200, 'fond': 200, 'equipment': 200, 'troma': 200, 'amounts': 200, 'elvis': 200, 'recording': 200, 'barney': 200, 'consistent': 200, 'norris': 200, 'coast': 200, 'communist': 200, 'crucial': 200, 'dozens': 200, 'dynamic': 200, 'pearl': 200, 'helicopter': 199, 'maintain': 199, 'historically': 199, 'gangsters': 199, 'kicked': 199, 'cliche': 199, 'fooled': 199, 'divorce': 199, 'emphasis': 199, 'passionate': 199, 'focusing': 199, 'griffith': 199, 'namely': 199, 'impress': 199, 'tonight': 199, 'bold': 199, 'ears': 199, 'garbo': 199, 'dating': 199, 'financial': 199, 'forms': 199, 'rising': 199, 'philosophical': 199, 'cassavetes': 199, 'cagney': 199, 'hates': 198, 'splatter': 198, 'knight': 198, 'advise': 198, 'dentist': 198, 'cinematographer': 198, '1995': 198, 'forty': 198, 'march': 198, 'mountains': 198, 'vivid': 197, 'marion': 197, 'link': 197, 'agrees': 197, 'endings': 197, 'vice': 197, 'tender': 197, 'slave': 197, 'florida': 197, 'fed': 197, 'unoriginal': 196, 'justify': 196, 'bonus': 196, 'rotten': 196, 'trailers': 196, 'edgar': 196, 'boris': 196, 'karloff': 196, 'hart': 196, 'scores': 196, 'blond': 196, 'widow': 196, 'worn': 195, 'mentioning': 195, 'album': 195, 'exotic': 195, 'practice': 195, 'wore': 195, 'similarities': 195, 'glasses': 195, 'thoughtful': 195, 'convincingly': 195, 'tank': 195, 'graham': 195, 'blew': 195, 'discuss': 195, 'urge': 195, 'josh': 195, 'bang': 195, 'overrated': 195, 'streisand': 195, 'masterful': 194, 'potentially': 194, 'stevens': 194, 'journalist': 194, 'mistress': 194, 'plausible': 194, 'mason': 194, 'hilariously': 194, 'pretending': 194, 'abysmal': 194, 'homeless': 194, 'advance': 194, 'mail': 194, 'responsibility': 194, 'classes': 194, 'developing': 194, 'borrowed': 194, 'ellen': 194, 'considerable': 194, 'transformation': 194, 'lust': 194, 'harvey': 193, 'downhill': 193, 'website': 193, 'ease': 193, 'doug': 193, 'drawing': 193, 'survivors': 193, 'square': 193, 'deliberately': 193, 'bite': 193, 'custer': 193, 'perry': 193, 'bronson': 193, 'charlotte': 193, 'triumph': 193, 'bo': 193, 'bridges': 193, 'sounding': 193, 'subplots': 193, 'creativity': 193, 'survived': 193, 'steel': 193, 'improve': 192, 'frost': 192, 'puppets': 192, 'disappoint': 192, '1972': 192, 'francisco': 192, 'flop': 192, 'dinosaur': 192, 'resemblance': 192, 'baldwin': 192, 'nicholson': 192, 'tyler': 192, 'phil': 191, 'mill': 191, 'nostalgia': 191, 'mankind': 191, 'switch': 191, 'adorable': 191, 'mayor': 191, 'pan': 191, 'jesse': 191, 'cream': 191, 'displayed': 191, 'cheated': 191, 'taught': 191, 'enjoys': 191, 'adams': 190, 'defeat': 190, 'staring': 190, 'robinson': 190, 'advertising': 190, 'resort': 190, 'jeffrey': 190, 'disappeared': 190, 'awe': 190, 'rocket': 190, 'houses': 190, 'safety': 189, 'defense': 189, 'swimming': 189, 'riveting': 189, 'arrived': 189, 'shake': 189, 'guests': 189, 'birds': 189, 'overwhelming': 189, 'rat': 189, 'hideous': 189, 'tends': 189, 'sits': 189, 'edition': 189, 'wounded': 189, 'suitable': 189, 'uneven': 189, 'climactic': 189, 'couples': 189, 'forbidden': 189, 'inane': 189, 'wound': 189, 'nail': 189, 'reflect': 188, 'formulaic': 188, 'bay': 188, 'suggested': 188, 'drops': 188, 'ali': 188, '1930s': 188, 'careful': 188, 'warren': 188, 'lemmon': 188, 'exchange': 188, 'channels': 188, 'patients': 188, 'solution': 188, 'ties': 188, 'parties': 188, 'alongside': 188, 'brutally': 188, 'garfield': 188, 'stilted': 187, 'jews': 187, 'tad': 187, 'gate': 187, 'dolls': 187, 'daddy': 187, 'runner': 187, 'yelling': 187, 'vengeance': 187, 'directs': 187, 'treats': 187, 'contest': 187, 'cannibal': 187, 'demand': 187, 'reflection': 187, 'miyazaki': 187, 'watson': 187, 'trashy': 187, 'faster': 187, 'relations': 186, 'nervous': 186, 'feast': 186, 'fought': 186, 'jaws': 186, 'useful': 186, 'portion': 186, 'guessed': 186, 'underlying': 186, 'progresses': 186, 'command': 186, 'understated': 186, 'spacey': 186, 'morris': 186, 'li': 186, 'sappy': 185, 'corruption': 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123, 'incidents': 123, 'indication': 123, 'bike': 123, 'mothers': 123, 'pierce': 123, '1955': 123, 'narrow': 123, 'defeated': 123, 'grinch': 123, 'abruptly': 123, 'evans': 123, 'doris': 123, 'motive': 123, 'mall': 123, 'bread': 122, 'dub': 122, 'politicians': 122, 'camcorder': 122, 'partners': 122, 'lasts': 122, 'users': 122, 'galaxy': 122, 'shades': 122, 'comparisons': 122, 'handles': 122, 'spectacle': 122, 'escaping': 122, 'flashy': 122, 'aboard': 122, 'themed': 122, 'foolish': 122, 'homes': 122, 'anticipation': 122, 'pirate': 122, 'mama': 122, 'males': 122, 'automatically': 122, 'stairs': 122, 'justified': 122, 'roommate': 122, '1933': 122, 'asia': 122, 'nolte': 122, 'hides': 122, 'hangs': 122, 'satirical': 122, 'startling': 121, 'conveniently': 121, 'hooker': 121, 'compete': 121, 'logan': 121, 'stardom': 121, 'eyre': 121, 'cannes': 121, 'sized': 121, 'phenomenon': 121, 'wright': 121, 'cultures': 121, 'toole': 121, '2008': 121, 'sherlock': 121, 'pilots': 121, 'scratch': 121, 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115, 'selected': 114, 'scariest': 114, 'janet': 114, 'arrest': 114, 'abraham': 114, 'gear': 114, 'insurance': 114, 'funding': 114, 'expectation': 114, 'palance': 114, 'gilbert': 114, 'divorced': 114, 'foundation': 114, 'flower': 114, 'cbs': 114, 'stalking': 114, 'trace': 114, 'vehicles': 114, 'sinking': 114, 'regularly': 114, 'lang': 114, 'trains': 114, 'laurence': 114, 'oldest': 114, 'potter': 114, 'intricate': 114, '1985': 114, 'horrified': 114, 'diverse': 114, 'outline': 114, 'tcm': 114, 'contribute': 114, 'um': 114, 'paranormal': 114, 'brainless': 114, 'hawk': 114, 'phillip': 114, 'jules': 114, 'fiancée': 114, 'decidedly': 114, 'freaking': 114, 'goals': 114, 'interact': 113, 'satisfaction': 113, 'accepting': 113, 'approached': 113, 'turd': 113, 'mobile': 113, 'assignment': 113, 'concentrate': 113, 'unsatisfying': 113, 'ark': 113, 'charms': 113, 'bennett': 113, 'confronted': 113, 'physics': 113, 'tendency': 113, 'rice': 113, 'sorely': 113, 'grip': 113, 'pretends': 113, 'cents': 113, 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112, 'cameraman': 112, 'carla': 112, 'demonstrate': 112, 'jersey': 112, 'invention': 112, 'architect': 112, 'garland': 112, 'grandma': 111, 'insights': 111, 'misfortune': 111, 'inherent': 111, 'improbable': 111, 'garner': 111, 'predictably': 111, 'sells': 111, 'session': 111, 'parallels': 111, 'translate': 111, 'pam': 111, 'promoted': 111, 'bowl': 111, 'sixth': 111, 'determination': 111, 'preparing': 111, 'edison': 111, 'segal': 111, 'stumble': 111, 'shelley': 111, 'ominous': 111, 'posing': 111, 'heels': 111, 'license': 111, 'rebels': 111, 'candidate': 111, 'lola': 111, 'pathos': 111, 'merchant': 111, 'everett': 111, 'detract': 111, 'joanna': 111, 'sickening': 111, 'stream': 111, 'artwork': 111, 'rapid': 111, 'flowers': 111, 'screwball': 111, 'ladder': 111, 'alison': 111, 'underdog': 111, 'criticize': 110, 'mouthed': 110, 'practical': 110, '300': 110, 'ghoulies': 110, 'booth': 110, 'ton': 110, 'wheel': 110, 'questioning': 110, 'rampage': 110, 'basinger': 110, 'atomic': 110, 'ebert': 110, 'explosive': 110, 'eagle': 110, 'incapable': 110, 'remembers': 110, 'elliott': 110, 'overbearing': 110, 'clash': 110, 'swearing': 110, 'mitch': 110, 'stabbed': 110, 'delighted': 110, 'scored': 110, 'juliette': 110, 'rainy': 110, 'spaceship': 110, 'possess': 110, 'exit': 110, 'exploits': 110, 'unimaginative': 110, 'mouths': 110, 'dixon': 110, 'leap': 110, 'vintage': 110, 'malone': 110, 'corporation': 110, 'entries': 109, 'overblown': 109, 'belle': 109, 'isolation': 109, 'moses': 109, 'observation': 109, 'august': 109, 'mccoy': 109, 'youthful': 109, 'switched': 109, 'pound': 109, 'compliment': 109, 'lynn': 109, 'shifts': 109, 'interviewed': 109, 'apply': 109, 'safely': 109, 'downs': 109, 'associate': 109, 'affleck': 109, 'pounds': 109, 'hybrid': 109, 'additionally': 109, 'vanessa': 109, 'aesthetic': 109, 'mirrors': 109, 'seedy': 109, 'auto': 109, 'destructive': 109, 'owes': 109, '1975': 109, 'bulk': 109, 'drab': 109, 'extensive': 109, 'cinemas': 109, 'amir': 109, 'ustinov': 109, 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107, 'frontier': 107, 'shanghai': 107, 'ensure': 107, 'othello': 107, 'origins': 107, 'sane': 107, 'stuffed': 107, 'relevance': 107, 'elm': 107, 'immigrant': 107, 'resembling': 107, 'wastes': 107, 'nathan': 107, 'classy': 107, 'immense': 107, 'chronicles': 107, 'unsure': 107, 'clarke': 107, 'breathing': 107, 'ghetto': 107, 'sparks': 107, 'sought': 107, 'realities': 107, 'fills': 107, 'uniform': 107, 'feeding': 107, 'gates': 107, 'swallow': 107, 'paltrow': 107, 'dexter': 107, 'harrowing': 107, 'lo': 107, 'linear': 107, 'filthy': 107, 'rory': 107, 'rugby': 107, 'boogie': 107, 'subsequently': 107, 'comprehend': 107, 'subway': 107, 'iq': 107, 'goodman': 107, 'kermit': 107, 'abortion': 107, 'veterans': 107, 'rainbow': 107, 'hercules': 106, 'politician': 106, 'scenarios': 106, 'distraction': 106, 'noticeable': 106, 'lightning': 106, 'organization': 106, 'buzz': 106, 'vignettes': 106, 'sharon': 106, 'statements': 106, 'poses': 106, 'excess': 106, 'otto': 106, 'dramatically': 106, 'crawl': 106, 'intensely': 106, 'reviewed': 106, 'alvin': 106, 'photographs': 106, 'coffin': 106, 'experiencing': 106, 'embrace': 106, 'encountered': 106, 'collector': 106, 'aiming': 106, 'wang': 106, 'adaption': 106, 'cab': 106, 'repressed': 106, 'korea': 106, 'lundgren': 106, 'begging': 105, 'conveyed': 105, 'pause': 105, 'believability': 105, 'employee': 105, 'remembering': 105, 'stella': 105, 'graves': 105, 'gangs': 105, 'distress': 105, 'israeli': 105, 'spit': 105, 'caper': 105, 'document': 105, 'knightley': 105, 'newcomer': 105, 'werewolves': 105, 'crosses': 105, 'embarrassingly': 105, 'headache': 105, 'legitimate': 105, 'obtain': 105, 'brat': 105, 'freaky': 105, 'billing': 105, 'baron': 105, 'crippled': 105, 'bikini': 105, 'gestures': 105, 'rapist': 105, 'dumped': 105, 'grabbed': 105, 'gaps': 105, 'wash': 105, 'arguments': 105, 'lighter': 105, 'casper': 105, 'darren': 105, 'landed': 105, 'collective': 105, 'liz': 105, 'creek': 105, 'taboo': 105, 'downfall': 105, 'definitive': 105, 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103, 'raging': 103, 'euro': 103, 'insults': 103, 'disorder': 103, 'pizza': 103, 'slim': 103, 'hyper': 103, 'estranged': 103, 'id': 103, 'uniforms': 103, 'theatres': 103, 'clara': 103, 'colleague': 103, 'campus': 103, 'lumet': 103, 'severed': 103, 'confuse': 103, 'weaker': 103, 'exploit': 103, 'lucille': 103, 'luis': 103, 'fort': 103, 'telephone': 103, 'frontal': 103, 'sweeping': 103, 'stalker': 103, 'arguing': 102, 'arquette': 102, 'noteworthy': 102, 'engine': 102, 'qualify': 102, 'whites': 102, 'flimsy': 102, 'banter': 102, 'theories': 102, 'midst': 102, 'carson': 102, 'contempt': 102, 'seductive': 102, 'sentences': 102, 'protection': 102, 'sgt': 102, 'liar': 102, 'sales': 102, 'kent': 102, '1934': 102, 'resume': 102, 'discussed': 102, 'earnest': 102, 'agony': 102, 'robbers': 102, 'slashers': 102, 'cheers': 102, 'slapped': 102, 'abound': 102, 'flames': 102, 'tolerance': 102, 'titular': 102, 'stargate': 102, 'gina': 102, 'gigantic': 102, 'pointing': 102, 'vanity': 102, 'newer': 102, 'exploited': 102, 'novelty': 102, 'possesses': 102, 'assembled': 102, 'pedestrian': 102, 'cutter': 102, 'tormented': 102, 'smash': 102, 'injury': 101, 'benefits': 101, 'fluid': 101, 'fodder': 101, 'meandering': 101, '1992': 101, 'psychedelic': 101, 'colored': 101, 'rounds': 101, 'verbal': 101, 'promptly': 101, 'deciding': 101, 'whiny': 101, 'leno': 101, 'missile': 101, 'swinging': 101, 'seats': 101, 'macabre': 101, 'soderbergh': 101, 'coward': 101, 'heather': 101, 'du': 101, 'glaring': 101, 'rightly': 101, 'routines': 101, 'climb': 101, 'redundant': 101, 'patty': 101, 'glimpses': 101, 'daffy': 101, 'rewarded': 101, 'jew': 101, 'esquire': 101, 'ivan': 101, 'pimp': 101, 'miami': 101, 'flowing': 101, 'whining': 101, 'distracted': 101, 'chop': 101, 'kinnear': 101, 'mythology': 101, 'admired': 101, 'herbert': 101, 'iran': 101, 'bye': 101, 'fuel': 101, 'kilmer': 101, 'indifferent': 101, 'bacall': 101, 'stephanie': 101, 'unfamiliar': 101, 'verdict': 101, 'understatement': 101, 'vivian': 101, 'vaughn': 101, 'spies': 101, 'achieves': 101, 'cells': 100, 'composition': 100, 'unattractive': 100, 'unsympathetic': 100, 'gravity': 100, 'introducing': 100, 'notices': 100, 'investigator': 100, 'rider': 100, 'intact': 100, 'republic': 100, 'profoundly': 100, 'shameless': 100, 'experts': 100, 'wai': 100, 'dumbest': 100, 'shocks': 100, '1957': 100, 'melody': 100, 'pocket': 100, 'patriotic': 100, 'defies': 100, 'reviewing': 100, 'trivial': 100, 'fragile': 100, 'weary': 100, 'blamed': 100, 'skit': 100, 'norton': 100, 'offbeat': 100, 'sen': 100, 'giants': 100, 'poet': 100, 'lends': 100, 'products': 100, 'bust': 100, 'zane': 100, 'awareness': 100, 'trauma': 100, 'breast': 100, 'artistry': 100, 'weaver': 100, 'sergio': 100, 'murky': 100, 'mick': 100, 'ideals': 100, 'characterisation': 100, 'professionals': 100, 'kinski': 100, 'lupino': 100, 'locate': 100, 'gromit': 100, 'pokemon': 100, 'granger': 100, 'excuses': 99, 'jude': 99, 'covering': 99, 'romances': 99, 'hawn': 99, 'neglected': 99, 'substantial': 99, 'dragons': 99, 'exclusively': 99, 'void': 99, 'flip': 99, 'packs': 99, 'thirties': 99, 'unusually': 99, 'plants': 99, 'distract': 99, 'salesman': 99, 'grayson': 99, 'acknowledge': 99, 'doyle': 99, 'stations': 99, 'joyce': 99, 'sophie': 99, 'demonstrated': 99, 'keys': 99, 'christy': 99, 'jolie': 99, 'bridget': 99, 'gundam': 99, 'niven': 99, 'fruit': 99, 'betrayed': 99, 'dealers': 99, 'sebastian': 99, 'willard': 99, 'bubble': 99, 'debra': 99, 'aztec': 99, 'frantic': 99, 'variation': 98, 'adrian': 98, 'climbing': 98, 'targets': 98, 'surf': 98, 'ignoring': 98, 'acquired': 98, 'stoned': 98, 'eggs': 98, 'valid': 98, 'chaotic': 98, 'rational': 98, 'consequently': 98, 'clerk': 98, 'update': 98, 'mysteriously': 98, 'costner': 98, 'affecting': 98, 'cloud': 98, 'monica': 98, 'celebrated': 98, 'viewpoint': 98, 'cons': 98, 'advised': 98, 'robertson': 98, 'addressed': 98, 'graveyard': 98, 'groundbreaking': 98, 'foil': 98, 'interrupted': 98, 'assumes': 98, 'alicia': 98, 'goings': 98, 'lansbury': 98, 'benny': 98, 'rejects': 98, 'penelope': 98, 'reverse': 98, 'parodies': 98, 'malkovich': 98, 'digging': 98, 'beg': 98, 'wonderland': 98, 'sucking': 98, 'madison': 98, 'fuzzy': 98, 'influential': 98, 'economic': 98, 'sporting': 98, 'jacob': 98, 'sabrina': 98, 'feminine': 98, 'cheadle': 98, 'meteor': 98, 'nina': 98, 'crooked': 97, 'employees': 97, 'tightly': 97, 'vcr': 97, 'settled': 97, 'rebecca': 97, 'towns': 97, 'foremost': 97, 'joining': 97, 'excellently': 97, 'darwin': 97, 'resulted': 97, 'puppy': 97, 'pros': 97, 'sterling': 97, 'admission': 97, 'tara': 97, 'optimistic': 97, 'brien': 97, 'ricky': 97, '1000': 97, 'redneck': 97, 'updated': 97, '1953': 97, 'fontaine': 97, 'phase': 97, 'cliched': 97, 'messing': 97, 'subtext': 97, 'tax': 97, 'claus': 97, 'witnessing': 97, 'sweden': 97, 'disregard': 97, 'bearing': 97, 'unconventional': 97, 'dante': 97, 'evokes': 97, 'principals': 97, 'lunatic': 97, 'micheal': 97, 'reflected': 97, 'convict': 97, 'rambling': 97, 'prot': 97, 'antagonist': 97, 'masked': 97, 'numbing': 97, 'unnatural': 97, 'cobb': 97, 'springer': 97, 'robocop': 97, 'bach': 97, 'crown': 96, 'comeback': 96, 'baddies': 96, 'circles': 96, 'consist': 96, 'battling': 96, 'cronenberg': 96, 'rodney': 96, 'coburn': 96, 'georgia': 96, 'laden': 96, 'furniture': 96, 'carnival': 96, 'flows': 96, 'profile': 96, 'dashing': 96, 'suggesting': 96, 'hysterically': 96, 'immigrants': 96, 'marines': 96, 'fence': 96, 'senator': 96, 'fritz': 96, 'letdown': 96, 'varied': 96, 'payoff': 96, 'instances': 96, 'eagerly': 96, 'sweat': 96, 'enigmatic': 96, 'dove': 96, 'scorpion': 96, 'ala': 96, 'tomb': 96, 'generate': 96, 'plodding': 96, 'daylight': 96, 'omen': 96, 'faded': 96, 'borrow': 96, 'lavish': 96, 'engineer': 96, 'hmm': 96, 'spelling': 96, 'boone': 96, 'salvation': 96, 'courtesy': 96, 'capacity': 96, 'rukh': 96, 'villainous': 96, 'holm': 96, 'lucio': 96, 'judges': 96, 'barbra': 96, 'hans': 96, 'sour': 96, 'nero': 96, 'dodgy': 95, 'complained': 95, 'vividly': 95, 'lopez': 95, 'blooded': 95, 'elder': 95, 'produces': 95, 'ambiguity': 95, 'clunky': 95, 'replacement': 95, 'warrant': 95, 'ing': 95, 'quasi': 95, 'kaufman': 95, 'brow': 95, 'auteur': 95, 'diamonds': 95, 'threads': 95, 'raj': 95, 'sheep': 95, 'phoenix': 95, 'sarcasm': 95, 'releasing': 95, 'stern': 95, 'plate': 95, 'grier': 95, 'starters': 95, 'vance': 95, 'stake': 95, 'socially': 95, 'bombing': 95, 'juan': 95, 'sided': 95, 'thereby': 95, 'sensational': 95, 'hmmm': 95, 'girlfriends': 95, 'misunderstood': 95, 'hating': 95, 'horizon': 95, 'narrated': 95, 'admiration': 95, 'glenda': 95, 'mega': 95, 'timberlake': 95, 'cape': 95, 'detroit': 95, 'excruciatingly': 95, 'chat': 95, 'henchmen': 95, 'ritchie': 95, 'sleeper': 95, 'graduation': 95, 'october': 95, 'norma': 95, 'wherever': 94, 'motions': 94, 'hooper': 94, 'overboard': 94, 'aided': 94, 'layered': 94, 'rivals': 94, 'strung': 94, 'shotgun': 94, 'mystical': 94, 'reminding': 94, 'bloom': 94, 'liberty': 94, 'dive': 94, 'depictions': 94, 'seduce': 94, 'boarding': 94, 'lampoon': 94, 'abstract': 94, 'explodes': 94, 'crosby': 94, 'pbs': 94, 'romanian': 94, 'camping': 94, 'shemp': 94, 'uncanny': 94, 'cursed': 94, 'observe': 94, 'timon': 94, 'indicate': 94, 'jurassic': 94, 'stefan': 94, 'shockingly': 94, 'comparable': 94, 'sketches': 94, 'versa': 94, 'wartime': 94, 'elected': 94, 'scratching': 94, 'shirts': 94, 'roughly': 94, 'chaney': 94, 'arrow': 94, 'su': 94, 'diary': 93, 'gloomy': 93, 'lonesome': 93, 'herman': 93, 'incestuous': 93, 'denouement': 93, 'warehouse': 93, 'horn': 93, 'ripoff': 93, 'nanny': 93, 'followers': 93, 'controls': 93, 'manners': 93, 'sustain': 93, 'se': 93, 'executives': 93, 'lieutenant': 93, 'apt': 93, 'holland': 93, 'lurking': 93, 'whoopi': 93, 'drill': 93, 'resistance': 93, 'feeble': 93, 'entrance': 93, 'momentum': 93, 'kris': 93, 'subdued': 93, 'regime': 93, 'confession': 93, 'ads': 93, 'contestants': 93, 'trigger': 93, 'pains': 93, 'gamera': 93, 'deliciously': 93, 'excellence': 93, 'fiennes': 93, 'paula': 93, 'marx': 93, 'parking': 93, 'warns': 93, 'gee': 93, 'cocaine': 93, 'shawn': 93, 'expertly': 93, 'paths': 93, 'motel': 93, 'knocks': 93, 'captivated': 93, 'scarier': 93, 'denis': 93, '1967': 93, 'gein': 93, 'phoebe': 93, 'brendan': 92, 'precise': 92, 'verge': 92, 'insomnia': 92, 'offend': 92, 'pans': 92, 'surrender': 92, 'array': 92, 'satanic': 92, 'ordeal': 92, 'shootout': 92, 'ally': 92, 'considers': 92, 'attenborough': 92, 'sigh': 92, 'upcoming': 92, 'daytime': 92, 'melancholy': 92, 'hunted': 92, 'adapt': 92, 'nuanced': 92, 'gap': 92, 'gathering': 92, 'swift': 92, 'policy': 92, 'wielding': 92, 'dynamite': 92, 'tierney': 92, 'consequence': 92, 'illusion': 92, 'chewing': 92, 'jose': 92, 'nonexistent': 92, 'carnage': 92, 'chill': 92, 'hostile': 92, 'mermaid': 92, 'smarter': 92, 'anticipated': 92, 'resolve': 92, 'streak': 92, 'tire': 92, 'overweight': 92, 'epics': 92, 'populated': 92, 'turmoil': 92, 'kristofferson': 92, 'preaching': 92, 'stretches': 92, 'angelo': 92, 'papers': 92, 'deliverance': 92, 'substitute': 92, 'participate': 92, 'forgetting': 92, 'psychologist': 92, 'symbols': 92, 'witted': 92, 'brett': 92, 'screened': 92, 'inclined': 91, 'liu': 91, 'smug': 91, 'committing': 91, 'attending': 91, 'ninety': 91, 'heap': 91, 'sleepy': 91, 'framing': 91, 'toned': 91, 'howling': 91, 'ghastly': 91, 'prostitution': 91, 'toxic': 91, 'underdeveloped': 91, 'pressed': 91, 'philadelphia': 91, 'fanatic': 91, 'edwards': 91, 'tuned': 91, 'services': 91, 'wee': 91, 'eater': 91, 'mixes': 91, 'cracks': 91, 'firth': 91, 'perceived': 91, 'nuances': 91, 'despise': 91, 'hackman': 91, 'woven': 91, 'maintains': 91, 'timed': 91, 'sundance': 91, 'hinted': 91, 'officials': 91, 'whore': 91, 'honey': 91, 'summed': 91, 'des': 91, 'tones': 91, 'peggy': 91, 'ww2': 91, 'georges': 91, 'inaccuracies': 91, 'bachelor': 91, 'duel': 91, 'renee': 91, 'shylock': 91, 'fleshed': 91, 'degrees': 91, 'sensibility': 91, 'coup': 91, 'palm': 90, 'stereotyped': 90, 'liberties': 90, 'dismiss': 90, 'distinctive': 90, 'centuries': 90, 'emmy': 90, 'sunset': 90, 'mclaglen': 90, 'borders': 90, 'nutshell': 90, 'unclear': 90, 'rehash': 90, 'cap': 90, 'gosh': 90, 'lambert': 90, 'festivals': 90, 'respond': 90, 'dynamics': 90, 'bittersweet': 90, 'rivers': 90, 'boobs': 90, 'atlantic': 90, 'fulfill': 90, 'rendering': 90, 'teddy': 90, 'file': 90, 'rousing': 90, 'guardian': 90, 'slept': 90, 'occupied': 90, 'unanswered': 90, 'youngsters': 90, 'lestat': 90, 'bias': 90, 'din': 90, 'seinfeld': 90, 'inserted': 90, 'canyon': 90, 'lange': 90, 'hannah': 90, 'preacher': 90, 'proceed': 90, 'climatic': 90, 'sensation': 90, 'bondage': 90, 'servants': 90, 'manipulation': 90, 'enhance': 90, 'tin': 90, 'chow': 90, 'click': 90, 'buttons': 90, 'tenant': 90, 'bats': 90, 'sammy': 90, 'pleasures': 90, 'facility': 90, 'sinks': 90, 'enduring': 90, 'stable': 90, 'rips': 90, 'franklin': 90, 'marilyn': 90, 'thug': 90, 'mannered': 89, 'renaissance': 89, 'confined': 89, 'di': 89, 'obscurity': 89, 'forgiveness': 89, 'clarity': 89, 'mobster': 89, 'anthology': 89, 'alexandra': 89, 'switches': 89, 'drum': 89, 'arranged': 89, 'todays': 89, 'article': 89, 'brazilian': 89, 'collar': 89, 'beard': 89, 'wrestler': 89, 'association': 89, 'stating': 89, 'reluctantly': 89, 'begs': 89, 'targeted': 89, 'enchanting': 89, 'customers': 89, 'readily': 89, 'perverse': 89, 'bend': 89, 'guinness': 89, 'fishburne': 89, 'sensual': 89, 'crooks': 89, 'progression': 89, 'chapters': 89, 'looney': 89, 'graduate': 89, 'bunuel': 89, 'openly': 89, 'clubs': 89, 'distinction': 89, 'increase': 89, 'forgets': 89, 'ceiling': 89, 'thunder': 89, 'hawke': 89, 'admits': 89, 'malcolm': 89, 'grisly': 89, 'criticized': 89, 'prop': 89, 'filmography': 89, 'portuguese': 89, 'hurry': 89, 'offense': 89, 'fassbinder': 89, 'penalty': 89, 'biko': 89, 'antwone': 89, 'injustice': 88, 'progressed': 88, 'counting': 88, 'taut': 88, 'pivotal': 88, 'han': 88, 'admitted': 88, 'sassy': 88, 'finney': 88, 'shuttle': 88, 'steele': 88, 'nauseating': 88, 'berkeley': 88, 'pen': 88, 'stupidest': 88, 'duh': 88, 'fascist': 88, 'understandably': 88, 'murderers': 88, 'forties': 88, 'solved': 88, 'runaway': 88, 'astronaut': 88, 'launch': 88, 'upbeat': 88, 'cheering': 88, 'therapy': 88, 'sincerity': 88, 'pitched': 88, 'banks': 88, '1951': 88, 'ramon': 88, 'cody': 88, 'fido': 88, 'pixar': 88, 'tho': 88, 'compensate': 88, 'scripting': 88, 'lens': 88, 'sant': 88, 'uma': 88, 'retro': 88, '1944': 88, 'shannon': 88, 'baked': 88, 'hypnotic': 88, 'godard': 88, 'staging': 88, 'surgeon': 88, 'wayans': 88, 'myrna': 88, 'evelyn': 88, 'drawings': 88, 'dietrich': 88, 'poirot': 88, 'peers': 88, 'daisy': 88, 'lorre': 88, 'allies': 88, 'magically': 88, 'revolving': 87, 'battlefield': 87, 'begun': 87, 'pornographic': 87, 'gooding': 87, 'civilians': 87, 'witchcraft': 87, 'inter': 87, 'candle': 87, 'overs': 87, 'talkie': 87, 'creeps': 87, 'greene': 87, 'vigilante': 87, 'increasing': 87, 'sufficient': 87, 'sync': 87, 'audrey': 87, 'relentlessly': 87, 'stab': 87, 'alleged': 87, 'burnt': 87, 'vibe': 87, 'keanu': 87, 'meredith': 87, 'pickford': 87, 'lt': 87, 'elmer': 87, 'waking': 87, 'merry': 87, 'anxious': 87, 'relating': 87, 'profit': 87, 'fierce': 87, 'popped': 87, 'backed': 87, 'watcher': 87, 'gillian': 87, 'imo': 87, 'schedule': 87, 'hesitate': 87, 'derived': 87, 'stalked': 87, 'wholesome': 87, 'prostitutes': 87, 'shelter': 87, 'stepmother': 87, 'redgrave': 87, 'lindsay': 87, 'jacques': 87, 'henson': 87, '27': 87, 'bait': 87, 'naschy': 87, 'italians': 86, 'laboratory': 86, 'zellweger': 86, 'curiously': 86, 'anchor': 86, 'surround': 86, 'kingsley': 86, 'distributed': 86, 'revolt': 86, 'camps': 86, 'sensitivity': 86, 'edmund': 86, 'hateful': 86, 'sexist': 86, 'nifty': 86, 'tilly': 86, 'inch': 86, 'sylvia': 86, 'robbed': 86, 'blink': 86, 'invested': 86, 'fellini': 86, 'boyer': 86, '1941': 86, 'nielsen': 86, 'sights': 86, 'zoey': 86, 'mcqueen': 86, 'officially': 86, 'henchman': 86, 'terrorism': 86, 'abroad': 86, 'patterson': 86, 'inconsistencies': 86, 'buffy': 86, 'platoon': 86, 'apple': 86, 'posey': 86, 'invite': 86, 'nightmarish': 86, 'strain': 86, 'bothers': 86, 'insert': 86, 'courageous': 86, 'programming': 86, 'spinal': 86, 'abomination': 86, 'mentor': 86, 'celebrities': 86, 'spoofs': 86, 'rio': 86, 'spice': 86, 'observations': 86, 'raines': 86, 'captive': 86, 'govinda': 86, 'bleed': 86, 'huppert': 86, 'devotion': 86, 'zeta': 86, 'fried': 86, 'pompous': 86, 'jodie': 86, 'rathbone': 86, 'dahmer': 86, 'relaxed': 86, 'zizek': 86, 'slower': 85, 'bart': 85, 'vader': 85, 'transported': 85, 'dragging': 85, 'espionage': 85, 'zany': 85, 'einstein': 85, 'traumatic': 85, 'emphasize': 85, 'polly': 85, 'sho': 85, 'courtroom': 85, 'favourites': 85, 'romania': 85, 'flame': 85, 'identified': 85, 'controlling': 85, '1966': 85, 'thirst': 85, 'instincts': 85, 'spotted': 85, 'prologue': 85, 'oblivious': 85, 'esther': 85, 'improvised': 85, 'abundance': 85, 'cain': 85, 'failures': 85, 'troopers': 85, 'enchanted': 85, 'dodge': 85, 'economy': 85, 'blockbusters': 85, 'ebay': 85, 'languages': 85, 'ample': 85, 'cheerful': 85, 'unfolding': 85, 'pairing': 85, 'monotonous': 85, 'transferred': 85, 'flew': 85, 'loretta': 85, 'lambs': 85, 'pub': 85, 'capote': 85, 'seth': 85, 'accompanying': 85, 'outset': 85, 'traditions': 85, '1942': 85, 'tolerate': 85, 'moriarty': 85, 'sonny': 85, 'ira': 85, 'quarters': 85, 'armageddon': 85, 'yell': 85, 'ramones': 85, 'katherine': 85, 'gulliver': 85, 'faint': 84, 'nearest': 84, 'judgement': 84, 'skeptical': 84, 'limp': 84, 'chamber': 84, 'dwarf': 84, 'faux': 84, 'bearable': 84, 'webb': 84, 'exploitative': 84, 'reckless': 84, 'superfluous': 84, 'rogue': 84, 'dandy': 84, 'rudy': 84, 'principles': 84, 'rant': 84, 'ambitions': 84, 'sickness': 84, 'perverted': 84, 'wan': 84, 'caroline': 84, 'corbett': 84, 'reno': 84, 'bitchy': 84, 'edges': 84, 'charges': 84, 'fundamental': 84, 'yells': 84, 'snappy': 84, 'swamp': 84, 'pose': 84, 'cheat': 84, 'ss': 84, 'gallery': 84, 'cassie': 84, 'mart': 84, 'gerald': 84, 'stripped': 84, 'frat': 84, 'borrows': 84, 'ostensibly': 84, 'proportions': 84, 'europeans': 84, 'patriot': 84, 'rookie': 84, 'weekly': 84, 'funky': 84, 'stink': 84, 'pursuing': 84, 'brynner': 84, 'monologues': 84, 'intend': 84, 'lizard': 84, 'interpretations': 84, 'burke': 84, 'fairness': 84, 'faye': 84, 'fart': 84, 'anton': 84, 'basil': 84, 'sins': 84, 'applause': 83, 'inadvertently': 83, 'nest': 83, 'networks': 83, 'bucket': 83, '1956': 83, '18th': 83, 'scriptwriter': 83, 'babette': 83, 'ensue': 83, 'accidental': 83, 'obstacles': 83, 'heath': 83, 'sidewalk': 83, 'flipping': 83, 'announced': 83, 'madman': 83, 'dern': 83, 'authors': 83, 'repeats': 83, 'lighthearted': 83, 'electricity': 83, 'serials': 83, 'combs': 83, 'insulted': 83, 'cynicism': 83, 'joint': 83, 'boiled': 83, 'shocker': 83, 'thurman': 83, 'hardest': 83, 'elliot': 83, 'governor': 83, 'scarface': 83, 'periods': 83, 'participants': 83, 'raid': 83, 'envy': 83, 'recurring': 83, 'fraud': 83, '1958': 83, 'allegedly': 83, 'request': 83, 'flashing': 83, 'bava': 83, 'schwarzenegger': 83, 'guinea': 83, 'puzzled': 83, 'impending': 83, 'gershwin': 83, 'achievements': 83, 'macdonald': 83, 'fanny': 83, 'dedication': 82, 'awakening': 82, 'brick': 82, 'protest': 82, 'smoothly': 82, 'chip': 82, 'railroad': 82, 'knocking': 82, 'denied': 82, 'custody': 82, 'bombed': 82, 'originals': 82, '26': 82, 'evoke': 82, 'amber': 82, 'demille': 82, 'shepard': 82, 'ignores': 82, 'bald': 82, 'casablanca': 82, 'laying': 82, 'listened': 82, 'marketed': 82, 'criticisms': 82, 'butch': 82, 'floyd': 82, 'cousins': 82, 'worms': 82, 'strained': 82, 'flee': 82, 'similarity': 82, 'detached': 82, 'seriousness': 82, 'idealistic': 82, 'hardened': 82, 'crushed': 82, 'deadpan': 82, 'omar': 82, 'winners': 82, 'bless': 82, 'boost': 82, 'confronts': 82, '1932': 82, 'roach': 82, 'ness': 82, 'ruining': 82, 'lombard': 82, 'deneuve': 82, 'client': 82, 'anonymous': 82, 'transforms': 82, 'dreaming': 82, 'slavery': 82, '1948': 82, 'angelina': 82, 'combining': 82, 'breathe': 82, 'prone': 82, 'phones': 82, 'transport': 82, 'effortlessly': 82, 'chevy': 82, 'informs': 82, 'polar': 82, 'applies': 82, 'evolved': 82, 'ariel': 82, 'knees': 82, 'shearer': 82, 'kar': 82, 'caligula': 82, 'mandy': 82, 'elijah': 81, 'explanations': 81, 'atrocity': 81, 'tacked': 81, 'characteristic': 81, '1947': 81, 'unconscious': 81, 'missions': 81, 'boundaries': 81, 'handicapped': 81, 'manic': 81, 'reported': 81, 'deanna': 81, 'sleeve': 81, 'assistance': 81, 'joker': 81, 'passenger': 81, 'remark': 81, 'happenings': 81, 'roosevelt': 81, 'revelations': 81, 'casually': 81, 'mythical': 81, 'confirmed': 81, 'significantly': 81, 'retrospect': 81, 'advances': 81, 'earliest': 81, 'greats': 81, 'gathered': 81, 'satellite': 81, 'overtones': 81, 'bills': 81, 'distinguished': 81, 'admirer': 81, 'dominate': 81, 'needing': 81, 'braveheart': 81, 'sheila': 81, 'gielgud': 81, 'reruns': 81, 'dudley': 81, 'attic': 81, 'zombi': 81, 'consumed': 81, 'crashed': 81, 'circa': 81, 'vincenzo': 81, 'radiation': 81, 'motivated': 81, 'spoiling': 81, '1949': 81, 'truman': 81, 'reject': 81, 'dusty': 81, '73': 81, 'beth': 81, 'largest': 81, 'deed': 81, 'blazing': 81, 'spotlight': 81, 'influences': 81, 'occult': 81, 'playful': 81, 'bachchan': 81, 'infinitely': 81, 'asterix': 81, 'awfulness': 81, 'hungarian': 81, 'falcon': 81, 'pa': 81, 'homicidal': 81, 'fetish': 81, 'bogus': 81, 'locke': 81, 'muslims': 80, 'villagers': 80, 'impeccable': 80, 'kidnaps': 80, 'cummings': 80, 'amato': 80, 'veronica': 80, 'cecil': 80, 'sniper': 80, 'nerdy': 80, 'butterfly': 80, 'shifting': 80, 'decency': 80, 'unappealing': 80, 'switching': 80, 'displaying': 80, 'perspectives': 80, 'arrogance': 80, 'bryan': 80, 'suicidal': 80, 'parrot': 80, 'manipulated': 80, 'bloke': 80, 'kathryn': 80, 'harvest': 80, 'fades': 80, 'marcus': 80, 'stirring': 80, 'peril': 80, 'contributes': 80, '250': 80, 'laser': 80, 'handy': 80, 'sections': 80, 'toby': 80, 'mortal': 80, 'charity': 80, 'hopeful': 80, 'telly': 80, 'incidental': 80, 'desk': 80, 'winchester': 80, 'tables': 80, 'fixed': 80, 'sacrifices': 80, 'deborah': 80, 'muscle': 80, 'conclusions': 80, 'irritated': 80, 'blaxploitation': 80, 'beethoven': 80, 'qualifies': 80, 'restraint': 80, 'keeper': 80, 'cavalry': 80, 'vega': 80, 'systems': 80, 'hugo': 80, 'saloon': 80, 'rouge': 80, 'kolchak': 80, 'carly': 80, 'scarlet': 79, 'kings': 79, 'counterpart': 79, 'hockey': 79, 'bimbo': 79, 'croc': 79, 'remainder': 79, 'commanding': 79, 'marathon': 79, 'chef': 79, 'forrest': 79, 'pee': 79, 'boxes': 79, 'counterparts': 79, 'throne': 79, 'inhabit': 79, 'secure': 79, 'diversity': 79, 'chips': 79, 'dash': 79, 'retirement': 79, 'ramsey': 79, 'jar': 79, 'necessity': 79, 'jock': 79, 'rko': 79, 'transitions': 79, 'cunning': 79, 'swanson': 79, 'unnecessarily': 79, 'district': 79, 'surrealism': 79, 'lubitsch': 79, 'crowded': 79, 'outcast': 79, 'realises': 79, 'camilla': 79, 'celebrate': 79, 'distinctly': 79, 'avoids': 79, 'elementary': 79, 'collapse': 79, 'downtown': 79, 'traps': 79, 'deception': 79, 'outlandish': 79, 'uneasy': 79, 'barber': 79, 'hairy': 79, 'superstar': 79, 'imho': 79, 'conflicted': 79, 'bosses': 79, 'ernie': 79, 'believer': 79, 'rene': 79, 'engagement': 79, 'watered': 79, 'dam': 79, 'labeled': 79, 'semblance': 79, 'glossy': 79, 'groove': 79, 'kindly': 79, 'ins': 79, 'bodyguard': 79, '1965': 79, 'observed': 79, 'fighters': 79, 'bon': 79, 'chavez': 79, 'hosts': 78, 'sack': 78, 'subtitled': 78, 'swiss': 78, 'thumb': 78, 'portions': 78, 'marrying': 78, 'lesbians': 78, 'rhys': 78, 'scattered': 78, 'shout': 78, 'spinning': 78, 'scheming': 78, 'cheung': 78, 'paragraph': 78, 'miraculously': 78, 'torch': 78, 'email': 78, 'ceremony': 78, 'neal': 78, 'wiped': 78, 'novelist': 78, 'noel': 78, 'serum': 78, 'excels': 78, 'theft': 78, 'darling': 78, 'sentinel': 78, 'dafoe': 78, 'dien': 78, 'talky': 78, 'loan': 78, 'risks': 78, 'graffiti': 78, 'relying': 78, 'sheets': 78, 'stones': 78, 'steaming': 78, 'jerky': 78, 'choir': 78, 'juice': 78, 'awry': 78, 'testing': 78, 'gasp': 78, 'lau': 78, 'unstable': 78, 'kinky': 78, 'octopus': 78, 'uptight': 78, 'awaiting': 78, 'truths': 78, 'clinic': 78, 'lorenzo': 78, 'slash': 78, 'vapid': 78, 'kathleen': 78, 'crook': 78, 'paycheck': 78, 'mash': 78, 'marlow': 78, 'padding': 78, 'implies': 78, 'lon': 78, 'harriet': 78, 'duncan': 78, 'federal': 78, '1943': 78, 'grin': 78, 'curtain': 78, 'stadium': 78, 'scarred': 77, 'register': 77, 'communism': 77, 'campfire': 77, 'randall': 77, 'houston': 77, 'astronauts': 77, 'dish': 77, 'sober': 77, 'hometown': 77, 'flock': 77, 'milland': 77, 'spawned': 77, 'developments': 77, 'chuckles': 77, 'irwin': 77, 'treating': 77, 'judith': 77, 'drain': 77, 'mills': 77, 'hes': 77, 'selleck': 77, 'imitate': 77, 'unwilling': 77, 'puns': 77, 'responds': 77, 'seal': 77, 'december': 77, 'chocolate': 77, 'anxiety': 77, 'sheridan': 77, 'kristin': 77, 'tackle': 77, 'liner': 77, 'determine': 77, 'wrongly': 77, 'abandon': 77, 'theodore': 77, 'balloon': 77, 'magician': 77, 'suave': 77, 'unfinished': 77, 'bastard': 77, 'inform': 77, 'manga': 77, 'rickman': 77, 'hellraiser': 77, 'dreamy': 77, 'lovingly': 77, 'penned': 77, 'spectrum': 77, 'lays': 77, 'shameful': 77, 'drugged': 77, 'mice': 77, '2009': 77, 'marvellous': 77, 'deemed': 77, 'grease': 77, 'lists': 77, 'laurie': 77, 'orphan': 77, 'edith': 77, 'robbing': 77, 'dario': 77, 'benjamin': 77, 'backwoods': 77, 'vinnie': 77, 'playwright': 77, 'perkins': 77, 'closure': 77, 'overnight': 77, 'insanely': 77, 'beowulf': 77, 'martino': 77, 'scarecrows': 77, 'andreas': 77, 'encouraged': 76, 'sorrow': 76, 'stereo': 76, 'wherein': 76, 'alot': 76, 'upstairs': 76, 'outlaw': 76, 'charlton': 76, 'pianist': 76, 'bio': 76, 'eleanor': 76, 'recipe': 76, 'photograph': 76, 'investment': 76, 'torment': 76, 'geoffrey': 76, 'fairbanks': 76, 'shakes': 76, 'worship': 76, 'asset': 76, 'pistol': 76, 'supportive': 76, 'dangers': 76, 'drift': 76, 'examine': 76, 'protective': 76, 'ranging': 76, 'admirably': 76, 'schizophrenic': 76, 'identities': 76, 'mabel': 76, 'pals': 76, 'drowned': 76, 'automatic': 76, '007': 76, 'policemen': 76, 'condemned': 76, 'mccarthy': 76, 'minnelli': 76, 'commendable': 76, 'mocking': 76, 'operating': 76, 'kazan': 76, 'subconscious': 76, 'chupacabra': 76, 'censorship': 76, 'skipping': 76, 'scoop': 76, 'paired': 76, 'farscape': 76, 'bandit': 76, 'shelves': 76, 'aura': 76, 'dirk': 76, 'gen': 76, 'chew': 76, 'masterson': 76, 'billie': 76, 'obscene': 76, 'vertigo': 76, 'structured': 76, 'capsule': 76, 'genie': 76, 'fiend': 76, 'duchovny': 76, 'slut': 76, 'shah': 76, 'overshadowed': 76, 'chopper': 76, 'artistically': 76, 'chops': 76, 'katie': 76, 'gig': 76, 'ninjas': 76, 'pope': 76, 'disappearance': 76, 'cowboys': 76, 'cancelled': 76, 'bert': 76, 'ratso': 76, 'poitier': 76, 'estevez': 76, 'sox': 76, 'interspersed': 76, 'bum': 76, 'solomon': 76, 'cal': 76, 'cliffhanger': 75, 'representative': 75, 'creations': 75, 'wynorski': 75, 'nerds': 75, 'geniuses': 75, 'bleeding': 75, 'cedric': 75, 'autobiography': 75, 'robber': 75, 'coleman': 75, 'retelling': 75, 'permanent': 75, 'jared': 75, 'virtue': 75, 'quantum': 75, 'conroy': 75, 'jedi': 75, 'overacts': 75, 'branch': 75, 'kite': 75, 'flamboyant': 75, 'spontaneous': 75, 'temptation': 75, 'schemes': 75, 'sasquatch': 75, 'splitting': 75, 'coincidences': 75, 'radar': 75, 'tits': 75, 'hilary': 75, 'techno': 75, 'promotion': 75, 'michaels': 75, 'cher': 75, 'raiders': 75, 'amid': 75, '1946': 75, 'goldblum': 75, 'distorted': 75, 'defining': 75, 'competing': 75, 'swords': 75, 'cohesive': 75, 'mercifully': 75, 'lengths': 75, 'brandon': 75, 'cuban': 75, 'lingering': 75, 'deeds': 75, 'kisses': 75, 'chemical': 75, 'millennium': 75, 'plagued': 75, 'darkly': 75, 'kindness': 75, 'stakes': 75, 'hooks': 75, 'demeanor': 75, 'gym': 75, 'scheider': 75, 'seattle': 75, 'defending': 75, 'relates': 75, 'dangerously': 75, 'hunk': 75, 'beforehand': 75, 'rebellion': 75, 'rowlands': 75, 'violently': 75, 'jagger': 75, 'aerial': 75, 'skating': 75, 'wolves': 75, 'carriage': 75, 'atwill': 75, 'stabbing': 75, 'thunderbirds': 75, 'reeve': 75, 'veronika': 75, 'letterman': 75, 'til': 74, 'continuous': 74, 'landmark': 74, 'coolest': 74, 'reasoning': 74, 'classified': 74, 'representing': 74, 'rapes': 74, 'pigs': 74, 'gilliam': 74, 'outrageously': 74, 'outdoor': 74, 'predicted': 74, 'caution': 74, 'overwrought': 74, 'counted': 74, 'recalls': 74, 'psychiatric': 74, 'mold': 74, 'discipline': 74, 'warfare': 74, 'whip': 74, 'jam': 74, 'chore': 74, 'animator': 74, 'boo': 74, 'noting': 74, 'conveying': 74, 'instructor': 74, 'slips': 74, 'granny': 74, 'loner': 74, 'junkie': 74, 'tested': 74, 'penis': 74, 'athletic': 74, 'accompany': 74, 'adventurous': 74, 'villa': 74, 'mcdowell': 74, 'basket': 74, 'operate': 74, 'managing': 74, 'staple': 74, 'concentration': 74, 'didnt': 74, '1938': 74, 'shaggy': 74, 'lightly': 74, 'ruled': 74, 'passive': 74, 'amateurs': 74, 'bites': 74, 'norwegian': 74, 'arkin': 74, 'searched': 74, 'farewell': 74, 'kerr': 74, 'clayton': 74, 'bash': 74, 'marvin': 74, 'slaughtered': 74, 'coyote': 74, 'fallon': 74, 'connecticut': 74, 'famed': 74, 'imprisoned': 74, 'springs': 74, 'blessed': 74, 'phrases': 74, 'correctness': 74, 'sydow': 74, 'yeti': 74, 'europa': 74, 'misty': 74, 'sylvester': 74, 'shady': 73, 'boards': 73, 'holden': 73, 'percent': 73, 'worrying': 73, 'midler': 73, 'visitor': 73, 'bloodshed': 73, 'shred': 73, 'avenge': 73, 'bout': 73, 'pulse': 73, 'anatomy': 73, 'naval': 73, 'pranks': 73, 'fantastically': 73, 'darcy': 73, 'lennon': 73, 'adequately': 73, 'snap': 73, 'mindset': 73, 'wilde': 73, 'judd': 73, 'luxury': 73, 'gesture': 73, 'trusted': 73, 'sweetheart': 73, 'denver': 73, 'sunk': 73, 'whimsical': 73, 'tricked': 73, 'walters': 73, 'lil': 73, 'voting': 73, 'inexperienced': 73, 'ram': 73, 'byrne': 73, 'occupation': 73, 'maurice': 73, 'depend': 73, 'cunningham': 73, 'induced': 73, 'laced': 73, 'thereafter': 73, 'compassionate': 73, 'quigley': 73, 'transparent': 73, 'domain': 73, 'knights': 73, 'instruments': 73, 'uncredited': 73, 'yearning': 73, 'tragically': 73, 'arizona': 73, 'napoleon': 73, 'somethings': 73, 'mac': 73, 'wahlberg': 73, 'stripper': 73, 'limbs': 73, 'dolemite': 73, 'categories': 73, 'sharks': 73, 'annoy': 73, 'interplay': 73, 'backyard': 73, 'awarded': 73, 'fashions': 73, 'comet': 73, 'wrestlemania': 73, 'solving': 73, 'mae': 73, 'patch': 73, 'predecessors': 73, 'pervert': 73, 'skateboarding': 73, 'lukas': 72, 'resurrection': 72, '1963': 72, 'liam': 72, 'cheaper': 72, 'raunchy': 72, 'drowning': 72, 'sophistication': 72, 'recreate': 72, 'quintessential': 72, 'greece': 72, 'ledger': 72, 'zoom': 72, 'renowned': 72, 'chamberlain': 72, 'sentenced': 72, 'talentless': 72, 'raimi': 72, 'ambiance': 72, 'irresponsible': 72, 'unleashed': 72, 'butchered': 72, 'mi': 72, 'bonds': 72, 'vulnerability': 72, 'levy': 72, 'varying': 72, 'dope': 72, 'electronic': 72, 'csi': 72, 'data': 72, 'agreement': 72, 'klaus': 72, 'suggestive': 72, 'impressions': 72, 'goof': 72, 'brooke': 72, 'gossip': 72, 'pegg': 72, 'stance': 72, 'maturity': 72, 'lara': 72, 'capt': 72, 'mans': 72, 'sammo': 72, 'disappearing': 72, 'revival': 72, 'stoic': 72, 'inhabited': 72, 'vet': 72, 'seuss': 72, 'dogma': 72, 'intellectually': 72, 'carroll': 72, 'enormously': 72, 'hutton': 72, 'grandpa': 72, 'borderline': 72, 'robotic': 72, 'incompetence': 72, 'arnie': 72, 'puerto': 72, 'log': 72, 'functions': 72, 'freddie': 72, 'nigel': 72, 'rodriguez': 72, 'hostel': 72, 'placing': 72, 'imply': 71, 'slam': 71, 'unremarkable': 71, 'foreboding': 71, 'fugitive': 71, 'shakespearean': 71, 'knee': 71, 'midget': 71, 'durbin': 71, 'versatile': 71, 'retrieve': 71, 'armor': 71, 'endured': 71, 'quaint': 71, 'fiasco': 71, 'usage': 71, '37': 71, 'icons': 71, 'lightweight': 71, 'underused': 71, 'harmony': 71, 'righteous': 71, 'promoting': 71, 'conductor': 71, 'goth': 71, 'galactica': 71, 'finishes': 71, 'owl': 71, 'arch': 71, 'brent': 71, 'reese': 71, 'skipped': 71, 'momma': 71, 'misplaced': 71, 'krueger': 71, 'preferably': 71, 'supports': 71, 'zealand': 71, 'puzzling': 71, 'overused': 71, 'expresses': 71, '1931': 71, 'maze': 71, 'soprano': 71, 'cheats': 71, 'justification': 71, 'britney': 71, 'reform': 71, 'giggle': 71, 'clarence': 71, 'gosling': 71, 'mole': 71, 'demi': 71, 'animators': 71, 'uncertain': 71, 'dreadfully': 71, 'constraints': 71, 'barbarian': 71, 'coen': 71, 'beau': 71, 'launched': 71, 'undertaker': 71, 'protecting': 71, 'disconnected': 71, 'weirdness': 71, 'wipe': 71, 'nailed': 71, 'baxter': 71, 'anguish': 71, 'earthquake': 71, 'dopey': 71, 'bags': 71, 'magazines': 71, 'praying': 71, 'bartender': 71, 'silverman': 71, 'peaks': 71, 'encourages': 71, 'prank': 71, 'bicycle': 71, 'collecting': 71, 'cannibalism': 71, 'saddest': 71, 'existential': 71, 'rae': 71, 'widowed': 71, 'luc': 71, 'trier': 71, 'mol': 71, 'badness': 70, 'mobsters': 70, 'tedium': 70, 'morse': 70, '98': 70, 'dependent': 70, 'consisting': 70, 'schumacher': 70, 'nineties': 70, 'overwhelmed': 70, 'premises': 70, 'zhang': 70, 'denying': 70, 'mathieu': 70, 'unravel': 70, 'alexandre': 70, 'shrink': 70, 'cam': 70, 'shamelessly': 70, 'replacing': 70, 'spears': 70, 'slumber': 70, 'lurid': 70, 'predictability': 70, 'entertains': 70, 'joking': 70, 'collaboration': 70, 'smallest': 70, 'lili': 70, 'lad': 70, 'trips': 70, 'posse': 70, 'temper': 70, 'healing': 70, 'prints': 70, 'bullies': 70, 'cos': 70, 'throats': 70, 'stalingrad': 70, 'truthful': 70, 'eliminated': 70, 'padded': 70, 'exorcism': 70, 'acclaim': 70, 'defy': 70, 'january': 70, 'stumbling': 70, 'cindy': 70, 'prevalent': 70, 'ongoing': 70, 'ghostly': 70, 'nt': 70, 'owe': 70, 'mustache': 70, 'rampant': 70, 'loony': 70, 'documents': 70, 'rightfully': 70, 'delve': 70, 'chloe': 70, 'showcases': 70, 'dismissed': 70, 'smack': 70, 'botched': 70, 'fitzgerald': 70, 'annoyance': 70, 'spoon': 70, 'enthralling': 70, 'crowe': 70, 'academic': 70, 'lawyers': 70, 'irons': 70, 'fleet': 70, 'efficient': 70, 'tools': 70, 'earns': 70, 'punks': 70, 'harlow': 70, 'intelligently': 70, 'echo': 70, 'tended': 70, 'miner': 70, 'fuss': 70, 'sheba': 70, 'crossfire': 69, 'atrocities': 69, 'occurring': 69, 'russ': 69, 'fiery': 69, 'squeeze': 69, 'brunette': 69, 'wrath': 69, 'hunky': 69, 'baffled': 69, 'plug': 69, 'sacrificed': 69, 'mo': 69, 'assassins': 69, 'martian': 69, 'airing': 69, 'manipulate': 69, 'insignificant': 69, 'guru': 69, 'tennis': 69, 'videotape': 69, 'psychologically': 69, 'architecture': 69, 'illustrate': 69, 'bursts': 69, 'nuance': 69, 'biological': 69, 'dominic': 69, 'ratio': 69, 'mack': 69, 'output': 69, 'vaudeville': 69, 'signature': 69, 'toss': 69, 'eg': 69, 'volumes': 69, 'teamed': 69, 'circuit': 69, 'nathaniel': 69, 'diesel': 69, 'watchers': 69, 'feared': 69, '400': 69, 'plotted': 69, 'crypt': 69, 'tempest': 69, 'geraldine': 69, 'dressler': 69, 'adultery': 69, 'echoes': 69, 'irresistible': 69, 'deepest': 69, 'tourists': 69, 'elusive': 69, 'patton': 69, 'carlyle': 69, 'interrogation': 69, 'dripping': 69, 'alarm': 69, 'dassin': 69, 'division': 69, 'torturing': 69, 'straw': 69, 'denial': 69, 'ventura': 69, 'inject': 69, 'herd': 69, 'remorse': 69, 'discount': 69, 'ate': 69, 'avenue': 69, 'leone': 69, 'blackmail': 69, 'bonding': 69, 'chiba': 69, 'vanilla': 69, 'stimulating': 69, 'crowds': 69, 'unnerving': 69, 'monks': 69, 'lecture': 69, 'raping': 69, 'bigfoot': 69, 'turtle': 69, 'drivers': 69, 'shoved': 69, 'olsen': 69, 'goofs': 69, 'honorable': 69, 'stares': 68, 'ada': 68, 'ashes': 68, 'dung': 68, 'monday': 68, 'priscilla': 68, 'warped': 68, 'haggard': 68, 'starship': 68, 'eliminate': 68, 'desolate': 68, 'catwoman': 68, 'arise': 68, 'amok': 68, 'unforgivable': 68, 'opposition': 68, 'strangest': 68, 'numbingly': 68, 'chock': 68, 'lions': 68, 'reels': 68, 'toe': 68, 'hypocrisy': 68, 'mister': 68, 'massey': 68, 'mockery': 68, 'approximately': 68, 'islands': 68, 'select': 68, 'bloodthirsty': 68, 'habits': 68, 'associates': 68, 'budding': 68, 'baddie': 68, 'fund': 68, 'positions': 68, 'reunite': 68, 'simpler': 68, 'steamy': 68, 'muted': 68, 'diving': 68, 'jessie': 68, 'interiors': 68, 'amoral': 68, 'alliance': 68, 'lex': 68, 'natasha': 68, 'refuge': 68, 'michel': 68, 'imagining': 68, 'sites': 68, 'customs': 68, 'meanings': 68, 'episodic': 68, 'revive': 68, 'drifter': 68, 'tensions': 68, 'confines': 68, 'corridors': 68, 'attendant': 68, 'transform': 68, 'emergency': 68, 'oblivion': 68, 'klein': 68, 'interpret': 68, 'poke': 68, 'anniversary': 68, 'fleeting': 68, 'plantation': 68, 'wendigo': 68, 'bynes': 68, 'thereof': 68, 'uncover': 68, 'deck': 68, 'richly': 68, 'allan': 68, 'layer': 68, 'vengeful': 68, 'gently': 68, 'intruder': 68, 'extraordinarily': 68, 'transplant': 68, 'freely': 68, 'brass': 68, 'lure': 68, 'gilmore': 68, 'dukes': 68, 'grating': 68, 'crispin': 68, 'democracy': 68, 'zorro': 68, 'diego': 68, 'maureen': 68, 'anita': 68, 'thelma': 68, 'carrot': 68, 'gi': 68, 'delta': 68, 'intellect': 68, 'stamp': 68, 'jefferson': 68, 'brit': 67, 'confederate': 67, 'worries': 67, 'pepper': 67, 'sensibilities': 67, 'angie': 67, 'castro': 67, 'nora': 67, 'grammar': 67, 'declared': 67, 'menu': 67, 'expressing': 67, 'ponder': 67, 'rugged': 67, 'saints': 67, 'lava': 67, 'entertainer': 67, 'woke': 67, 'bergen': 67, 'concrete': 67, 'loop': 67, 'transvestite': 67, 'operas': 67, 'concorde': 67, 'innuendo': 67, 'punished': 67, 'continuously': 67, 'pipe': 67, 'grandeur': 67, 'concentrates': 67, 'friendships': 67, 'epitome': 67, 'penchant': 67, 'billion': 67, '5th': 67, 'unlucky': 67, 'expanded': 67, 'moss': 67, 'hustler': 67, 'darth': 67, 'rooted': 67, 'blames': 67, 'unsuccessful': 67, 'projected': 67, 'supermarket': 67, '1920s': 67, 'adored': 67, 'jewelry': 67, 'appalled': 67, 'exhausted': 67, 'overt': 67, 'trait': 67, 'revolting': 67, 'stir': 67, 'rumble': 67, 'irving': 67, 'casted': 67, 'aniston': 67, 'gifts': 67, 'sweeney': 67, 'reiser': 67, 'scandal': 67, 'cockney': 67, 'jolly': 67, 'pryor': 67, 'slater': 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'discourse': 27, 'raining': 27, 'lenzi': 27, 'joyful': 27, 'siu': 27, 'compass': 27, 'cartoony': 27, 'toughness': 27, 'frying': 27, 'alfre': 27, 'nihilism': 27, 'prosper': 27, 'completes': 27, 'rockford': 27, 'barrett': 27, 'swap': 27, 'tomboy': 27, 'raisuli': 27, 'implausibility': 27, 'duplicate': 27, 'leatherface': 27, 'injecting': 27, 'revisionist': 27, 'aparna': 27, 'sweetest': 27, 'disliking': 27, 'gaiman': 27, 'minuscule': 27, '72': 27, 'tch': 27, 'adjusted': 27, 'dramatics': 27, 'smoked': 27, 'semen': 27, 'luna': 27, 'cosby': 27, 'rubbing': 27, 'sensationalism': 27, 'nuisance': 27, 'berkley': 27, 'godmother': 27, 'expelled': 27, 'mahoney': 27, 'mogul': 27, 'etched': 27, 'publish': 27, 'pussy': 27, 'greeting': 27, 'sleepless': 27, 'yorkers': 27, 'spiritually': 27, 'fanciful': 27, 'identifies': 27, 'abhay': 27, 'probing': 27, 'helms': 27, 'illustrious': 27, 'oss': 27, 'coy': 27, 'jets': 27, 'reside': 27, 'ribs': 27, 'blossoms': 27, 'glitz': 27, 'celestial': 27, 'scorned': 27, 'luigi': 27, 'catatonic': 27, 'multiplex': 27, 'bloodline': 27, 'ai': 27, 'toto': 27, 'distractions': 27, 'carlson': 27, '41': 27, 'index': 27, 'prevail': 27, 'greengrass': 27, 'avengers': 27, 'est': 27, 'invaluable': 27, 'swarm': 27, 'speculate': 27, 'littered': 27, 'sunken': 27, 'paradox': 27, 'fez': 27, 'edelman': 27, 'alma': 27, 'austere': 27, 'auditions': 27, 'sirens': 27, 'flooding': 27, 'davenport': 27, 'misused': 27, 'rejecting': 27, 'bassanio': 27, 'aristocrats': 27, 'princes': 27, 'dork': 27, 'freshly': 27, 'sidelines': 27, 'devos': 27, 'preferring': 27, 'metropolitan': 27, 'shovel': 27, 'earthy': 27, 'mono': 27, 'unabashed': 27, 'guerrero': 27, 'wrinkle': 27, 'valeria': 27, 'melts': 27, 'kindred': 27, 'ethnicity': 27, 'secular': 27, 'warts': 27, 'validity': 27, 'kelso': 27, 'suggestions': 27, 'scratched': 27, 'realms': 27, 'narrowly': 27, 'religiously': 27, 'plunges': 27, 'sweating': 27, 'yimou': 27, 'singularly': 27, 'abrasive': 27, 'abounds': 27, 'decor': 27, 'snatched': 27, 'promotes': 27, 'legit': 27, 'halle': 27, 'maxwell': 27, 'mumbles': 27, 'acrobatics': 27, 'upsets': 27, 'petersen': 27, 'collects': 27, 'courses': 27, 'yzma': 27, 'polarisdib': 27, 'incriminating': 27, 'nastiness': 27, 'sasha': 27, 'intricacies': 27, 'carved': 27, 'fluent': 27, 'dyer': 27, 'imamura': 27, 'rode': 27, 'expanding': 27, 'speedy': 27, 'kattan': 27, 'mounting': 27, 'potente': 27, 'ridgemont': 27, 'subterranean': 27, 'garlin': 27, 'carr': 27, 'heartstrings': 27, 'mahatma': 27, 'colleges': 27, 'emphasizes': 27, 'bambi': 27, 'diagnosis': 27, 'nostril': 27, 'mimicking': 27, 'arjun': 27, 'bel': 27, 'entice': 27, 'wei': 27, 'chap': 27, 'cease': 27, 'prosecution': 27, 'echoing': 27, 'wacko': 27, 'havana': 27, 'knute': 27, 'masala': 27, 'stupendous': 27, 'mononoke': 27, 'opting': 27, 'fixation': 27, 'rhonda': 27, 'healed': 27, 'railsback': 27, 'expressionless': 27, 'conrack': 27, 'incubus': 27, 'crave': 27, '½': 27, 'trepidation': 27, 'windshield': 27, 'moose': 27, 'urgent': 27, 'geoff': 27, 'feces': 27, 'boomer': 27, 'spartans': 27, 'conceive': 27, 'berkowitz': 27, 'burman': 27, 'watering': 27, 'stein': 27, 'pier': 27, 'rebuild': 27, 'pinocchio': 27, 'applegate': 27, 'plates': 27, 'evade': 27, 'dingy': 27, 'offenders': 27, 'hoppity': 27, 'inuyasha': 27, 'groves': 27, 'meals': 27, 'mcgovern': 27, 'hess': 27, 'rays': 27, 'hallam': 27, 'wodehouse': 27, 'farrow': 27, 'nipples': 27, 'brackett': 27, 'unedited': 27, 'meatball': 27, 'gwynne': 27, 'viola': 27, 'rajpal': 27, 'brite': 27, 'emote': 27, 'bardot': 27, 'buttgereit': 27, 'uninitiated': 27, 'audiard': 27, 'joo': 27, 'gabe': 27, 'heflin': 27, 'privacy': 26, 'carver': 26, 'friction': 26, 'grizzly': 26, 'rehashed': 26, 'drastic': 26, 'fledged': 26, 'glib': 26, 'suicides': 26, 'shutting': 26, 'suyin': 26, 'billboard': 26, 'onstage': 26, 'busty': 26, 'summon': 26, 'tamblyn': 26, 'kelsey': 26, 'hera': 26, 'bombshell': 26, 'deem': 26, 'knot': 26, 'camper': 26, 'boldly': 26, 'harassing': 26, 'patti': 26, 'enabled': 26, 'barefoot': 26, 'sporadic': 26, 'lyric': 26, 'commissioned': 26, 'thorne': 26, 'fruition': 26, 'lukewarm': 26, 'flourish': 26, 'mulligan': 26, 'macready': 26, 'flattering': 26, 'wily': 26, 'bannister': 26, 'dianne': 26, 'milan': 26, 'gorehounds': 26, 'adventurer': 26, 'weigh': 26, 'irritatingly': 26, 'tribeca': 26, 'founder': 26, 'punishing': 26, 'splits': 26, 'woodland': 26, 'sparked': 26, 'med': 26, 'vermont': 26, 'reprehensible': 26, 'cartwright': 26, 'syndication': 26, 'sanctimonious': 26, 'stripping': 26, 'uprising': 26, 'recommendable': 26, 'biographies': 26, '1918': 26, 'lucid': 26, 'scholars': 26, 'lovemaking': 26, 'danza': 26, 'asserts': 26, 'justifiably': 26, 'schooler': 26, 'banjo': 26, 'scriptwriting': 26, 'penniless': 26, 'dissatisfied': 26, 'furst': 26, 'pendleton': 26, 'malibu': 26, '63': 26, 'helga': 26, 'hairs': 26, 'julius': 26, 'unflattering': 26, 'proposition': 26, 'facilities': 26, 'heigl': 26, 'mathilda': 26, 'cylon': 26, 'afterwords': 26, 'sage': 26, 'agencies': 26, 'injustices': 26, 'wrecking': 26, 'boasted': 26, 'jonas': 26, 'fujimoto': 26, 'helmets': 26, 'didactic': 26, 'heavier': 26, 'humdrum': 26, 'budgetary': 26, 'physique': 26, 'mackenzie': 26, 'unmissable': 26, 'flags': 26, 'lizards': 26, 'robby': 26, 'rai': 26, 'reich': 26, 'immaculate': 26, 'morphs': 26, 'incorporating': 26, 'perseverance': 26, 'grosse': 26, '1921': 26, 'undertone': 26, 'crows': 26, 'unimpressed': 26, 'absurdities': 26, 'madam': 26, 'mindlessly': 26, 'pagan': 26, 'blackboard': 26, 'excluding': 26, 'implying': 26, 'pitcher': 26, 'parter': 26, 'afoul': 26, 'brushes': 26, 'inanimate': 26, 'funk': 26, 'pioneering': 26, 'sermon': 26, 'derives': 26, 'highs': 26, 'mediterranean': 26, 'outward': 26, 'aggravating': 26, 'claudio': 26, 'anthropologist': 26, 'domergue': 26, 'ordinarily': 26, 'ammo': 26, 'retaining': 26, 'reb': 26, 'blender': 26, 'windsor': 26, 'prompts': 26, 'avp': 26, 'diagnosed': 26, 'depressive': 26, 'nefer': 26, 'purest': 26, 'denholm': 26, 'astrid': 26, 'treaty': 26, 'defiantly': 26, 'rahul': 26, 'adjectives': 26, 'bulging': 26, 'cheery': 26, 'hunts': 26, 'electrician': 26, 'parenting': 26, 'conformity': 26, 'captivity': 26, 'vow': 26, 'roberto': 26, 'summation': 26, 'unjust': 26, 'crushes': 26, 'dwells': 26, 'anamorphic': 26, 'minion': 26, 'raja': 26, 'reactor': 26, 'pasts': 26, 'slicing': 26, 'perdition': 26, 'ouimet': 26, 'sceneries': 26, 'cages': 26, 'descendants': 26, 'payback': 26, 'raves': 26, 'darkwolf': 26, 'ukraine': 26, 'secretive': 26, 'broadly': 26, 'dissolves': 26, 'luciano': 26, 'curses': 26, 'lectures': 26, 'artfully': 26, 'rubble': 26, 'structures': 26, 'reformed': 26, 'mammoth': 26, 'archaic': 26, 'detour': 26, 'blacklisted': 26, 'handyman': 26, 'anakin': 26, 'playhouse': 26, 'practiced': 26, 'tomas': 26, 'overload': 26, 'disturbingly': 26, 'grieco': 26, 'abandoning': 26, 'promisingly': 26, 'heaps': 26, 'spaced': 26, 'premature': 26, 'horde': 26, 'marky': 26, 'footnote': 26, 'hash': 26, 'hooking': 26, 'milos': 26, 'prettier': 26, 'nearing': 26, 'icky': 26, 'merge': 26, 'erasmus': 26, 'introductory': 26, 'totoro': 26, 'undertaking': 26, 'hellman': 26, 'bureaucracy': 26, 'cleansing': 26, 'cavern': 26, 'glimpsed': 26, 'loyalties': 26, 'anachronisms': 26, 'inconceivable': 26, 'infused': 26, 'loveless': 26, 'inventiveness': 26, 'inexcusable': 26, 'neville': 26, 'unbiased': 26, 'mazzello': 26, 'impeccably': 26, 'fatima': 26, 'province': 26, 'pulitzer': 26, 'listeners': 26, 'summoned': 26, 'tucci': 26, 'appointment': 26, 'weirder': 26, 'shreck': 26, 'exits': 26, 'exuberance': 26, 'wired': 26, 'announcing': 26, 'darlene': 26, 'stanton': 26, 'cheryl': 26, 'ramshackle': 26, 'teaser': 26, 'pym': 26, 'chanting': 26, 'everlasting': 26, 'postcard': 26, 'awesomely': 26, 'coaching': 26, 'doodle': 26, 'hoax': 26, 'disgraced': 26, 'oozing': 26, 'chile': 26, 'blunders': 26, 'pabst': 26, 'statistics': 26, 'napier': 26, 'milked': 26, 'saps': 26, 'reinforce': 26, 'binge': 26, 'artifact': 26, 'ke': 26, 'scraping': 26, 'tumultuous': 26, 'perilous': 26, 'cylons': 26, 'perverts': 26, 'employers': 26, 'avenging': 26, 'implements': 26, 'daryl': 26, 'trader': 26, '1913': 26, 'consecutive': 26, 'bowman': 26, 'flailing': 26, 'shuts': 26, 'pledge': 26, 'vii': 26, 'suckers': 26, 'hahaha': 26, 'cercle': 26, 'elia': 26, 'downtrodden': 26, 'rugrats': 26, 'entails': 26, 'kristine': 26, 'kosugi': 26, 'blandly': 26, 'wed': 26, 'tubes': 26, 'otis': 26, 'mancuso': 26, 'prix': 26, 'aural': 26, 'boreanaz': 26, 'reigns': 26, 'declaring': 26, 'mccool': 26, 'consensus': 26, 'angrily': 26, 'tintin': 26, 'frosty': 26, 'chronologically': 26, 'caribe': 26, 'parasite': 26, 'binder': 26, 'weston': 26, 'aggressively': 26, 'paige': 26, 'stressful': 26, 'dent': 26, 'perils': 26, 'martel': 26, 'cutest': 26, 'ferguson': 26, 'martinez': 26, 'yadav': 26, 'astounded': 26, 'allegra': 26, 'clarkson': 26, 'prc': 26, 'mania': 26, 'johanna': 26, 'sculptor': 26, 'coco': 26, 'breckinridge': 26, 'gemser': 26, 'todesking': 26, 'trivialboring': 26, 'tibbs': 26, 'transfers': 25, 'anxiously': 25, 'respite': 25, 'postal': 25, 'jargon': 25, 'repercussions': 25, 'encompasses': 25, 'steep': 25, 'regions': 25, 'miyagi': 25, 'petulant': 25, 'fingersmith': 25, 'placid': 25, 'ophelia': 25, 'janine': 25, 'daredevil': 25, 'cub': 25, 'painters': 25, 'forewarned': 25, 'yvette': 25, 'twitch': 25, 'counselors': 25, 'contacted': 25, 'unprepared': 25, 'privy': 25, 'bennet': 25, 'tapestry': 25, 'sleek': 25, 'griffiths': 25, 'purposefully': 25, 'contradictory': 25, 'emergence': 25, 'cliffs': 25, 'ernesto': 25, 'rancher': 25, 'godfrey': 25, 'breakout': 25, 'impromptu': 25, 'insatiable': 25, 'pantomime': 25, 'consume': 25, 'saucy': 25, 'hangover': 25, 'assures': 25, 'symbolizes': 25, '1914': 25, 'minnesota': 25, 'nondescript': 25, 'prochnow': 25, 'advises': 25, 'snobs': 25, 'adjective': 25, 'hoops': 25, 'bugged': 25, 'indies': 25, 'gears': 25, 'narcissism': 25, 'dome': 25, 'rentals': 25, 'verma': 25, 'illegitimate': 25, 'steering': 25, 'mustang': 25, 'archetypes': 25, 'slaughterhouse': 25, 'neighborhoods': 25, 'businesses': 25, 'advantages': 25, 'wasp': 25, 'responding': 25, 'overdue': 25, 'erection': 25, 'elves': 25, 'monitors': 25, 'flicker': 25, 'plumbing': 25, 'purist': 25, 'whirlwind': 25, 'ramifications': 25, 'utilizes': 25, 'hijacked': 25, 'owens': 25, 'perpetrator': 25, 'tak': 25, 'reptile': 25, 'groans': 25, 'atrociously': 25, 'fanfare': 25, 'integration': 25, 'nitpick': 25, 'captivates': 25, 'aristocracy': 25, 'bedtime': 25, 'potatoes': 25, '1900': 25, 'defects': 25, 'concise': 25, 'seductress': 25, 'strays': 25, 'hurley': 25, 'cod': 25, 'fetus': 25, 'detracted': 25, 'sweets': 25, 'lorna': 25, 'implore': 25, 'standouts': 25, 'vignette': 25, 'heartedly': 25, 'maud': 25, 'inflicting': 25, 'compensation': 25, 'yankees': 25, 'saturn': 25, 'lease': 25, 'branches': 25, 'caps': 25, 'borne': 25, 'exchanging': 25, 'whalley': 25, 'gabrielle': 25, 'bonkers': 25, 'translating': 25, 'glitches': 25, 'sped': 25, 'ahh': 25, 'bidding': 25, 'infatuation': 25, 'roster': 25, 'flights': 25, 'grate': 25, 'twisty': 25, 'wiping': 25, 'jonathon': 25, 'suzumiya': 25, 'taboos': 25, 'endorse': 25, 'embodied': 25, 'egregious': 25, 'jolt': 25, 'poles': 25, 'motto': 25, 'dodging': 25, 'teal': 25, 'margret': 25, 'blanc': 25, 'katrina': 25, 'gateway': 25, 'homoerotic': 25, 'bouncy': 25, 'turf': 25, 'shrouded': 25, 'humanoids': 25, 'scatman': 25, 'presenter': 25, 'neve': 25, 'unsophisticated': 25, 'wilford': 25, 'chayefsky': 25, 'abysmally': 25, 'earthly': 25, 'brokeback': 25, 'rampling': 25, 'boiler': 25, 'irreversible': 25, 'analyzing': 25, 'initiation': 25, 'suckered': 25, 'droppingly': 25, 'auction': 25, 'misunderstand': 25, 'prudish': 25, 'fiancee': 25, 'afore': 25, 'shipped': 25, 'whodunnit': 25, 'unholy': 25, 'cancellation': 25, 'litter': 25, 'introspection': 25, 'wench': 25, 'saxophone': 25, 'lobotomy': 25, 'stupor': 25, 'comprises': 25, 'confessed': 25, 'ashore': 25, 'customary': 25, 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'stymieing': 1, 'psychosexually': 1, 'unsub': 1, 'telephoning': 1, 'quantico': 1, 'unkill': 1, 'luda': 1, 'snowboard': 1, 'sequals': 1, 'kb': 1, 'beyoncé': 1, 'noemi': 1, 'bae': 1, 'intercession': 1, 'bavier': 1, 'snazziest': 1, 'jogue': 1, 'mamãe': 1, 'trem': 1, 'loggins': 1, 'fowlds': 1, 'buttergeit': 1, 'nebesti': 1, 'jezdci': 1, 'jaroslav': 1, 'jezek': 1, 'tcha': 1, 'avari': 1, 'benjamenta': 1, 'inversely': 1, 'quijote': 1, 'erschbamer': 1, 'draftee': 1, 'lanoir': 1, 'qwak': 1, 'drion': 1, 'timsit': 1, 'thingamajigs': 1, 'belays': 1, 'prognostications': 1, 'yuzo': 1, 'koshiro': 1, 'bons': 1, 'faluttin': 1, 'thatn': 1, 'gwh': 1, 'tiw': 1, 'oporto': 1, 'pinho': 1, 'unawareness': 1, 'manichaean': 1, 'smsix': 1, 'caravagggio': 1, 'jasminder': 1, 'slowmo': 1, 'approporiately': 1, 'naturedness': 1, 'busness': 1, 'disapears': 1, 'playgroud': 1, 'traped': 1, 'colorous': 1, 'throlls': 1, 'popkin': 1, 'misstepped': 1, 'cammie': 1, 'hahahhaa': 1, 'vinessa': 1, 'collectibles': 1, 'mercan': 1, 'dede': 1, 'clarinetist': 1, 'sesler': 1, 'neuclear': 1, 'guiccioli': 1, 'childe': 1, 'missolonghi': 1, 'gambas': 1, 'unban': 1, 'ninjo': 1, 'torazo': 1, 'helpfulness': 1, 'pugilism': 1, 'habilities': 1, 'ff2': 1, 'ophül': 1, 'ohlrig': 1, 'kubrickysh': 1, 'carpathians': 1, 'maars': 1, 'amada': 1, 'sahalin': 1, 'anavel': 1, '07b': 1, 'gouf': 1, 'stedi': 1, 'stagnate': 1, 'progrmmer': 1, 'balky': 1, 'misconstrue': 1, 'stimulants': 1, 'exalting': 1, 'tattoine': 1, 'lukes': 1, 'dagoba': 1, 'demonio': 1, 'forebodings': 1, 'lamorghini': 1, 'wist': 1, 'dinasours': 1, 'seneca': 1, 'praetorian': 1, 'burrus': 1, 'bryne': 1, 'creasing': 1, 'excommunications': 1, 'amarna': 1, 'unzipped': 1, 'pachyderms': 1, 'uglying': 1, 'pppenheimer': 1, 'shatterer': 1, 'portrayalof': 1, 'audiocassette': 1, 'wanabees': 1, 'interning': 1, 'peliculam': 1, 'provisos': 1, 'lambent': 1, 'lamesters': 1, 'milfy': 1, 'unmasterfully': 1, 'stati': 1, 'putrefying': 1, 'softheaded': 1, 'iraquis': 1, 'yaaayyyy': 1, 'ballplaying': 1, '9it': 1, 'skynet': 1, 'homunculi': 1, 'eucharist': 1, 'escadrille': 1, 'yeeeecchh': 1, 'amplifier': 1, 'demonoïd': 1, 'rohna': 1, 'pintos': 1, 'citations': 1, 'counselled': 1, 'antwones': 1, 'mottos': 1, 'kman': 1, 'nolin': 1, 'angelyne': 1, 'censorial': 1, 'burtolucci': 1, 'jossi': 1, 'ashknenazi': 1, 'effortful': 1, 'clatter': 1, 'movie8': 1})

unique_words = len(word_frequency.keys())
print('Unique_words: ',f'{unique_words}')

Unique_words:  103774

positive_words = Counter()
negative_words = Counter()

for index, row in imdb.iterrows():
    words = row['Token']
    sentiment = row['sentiment']
    if sentiment == 1:
        positive_words.update(words)
    else:
        negative_words.update(words)
    
#Resources: 
#https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.iterrows.html
#https://www.kaggle.com/code/juicykn/imdb-movie-list-analysis-in-python-and-sql

top_positive_words = positive_words.most_common(10)
top_negative_words = negative_words.most_common(10)

print('Positive: ', top_positive_words)
print('\n')
print('Negative: ', top_negative_words)

Positive:  [('film', 42090), ('movie', 37844), ('one', 27312), ('like', 17709), ('good', 15020), ('great', 12961), ('story', 12931), ('time', 12745), ('well', 12724), ('see', 12271)]


Negative:  [('movie', 50090), ('film', 37579), ('one', 26273), ('like', 22451), ('even', 15243), ('good', 14717), ('bad', 14714), ('would', 14005), ('time', 12354), ('really', 12354)]

#Spliting the tupple we got earlier
positive_words, positive_counts = zip(*top_positive_words)
negative_words, negative_counts = zip(*top_negative_words)

#Charts----------------------------------------------------
fig, axs = plt.subplots(2,1,figsize=(10,8))

#Positive words plot
axs[0].bar(positive_words, positive_counts, color='green')
axs[0].set_title('Most Frequent Positive Words')
axs[0].set_ylabel('Frequency')

#Negative words plot
axs[1].bar(negative_words, negative_counts, color='red')
axs[1].set_title('Most Frequent Negative Words')
axs[1].set_ylabel('Frequency')

#Space between charts
plt.tight_layout(pad=4.0)
plt.show()           
           
#Resources:
# https://realpython.com/python-zip-function/#using-zip-in-python
# https://matplotlib.org/stable/index.html

#Splitting data into train 70%, validation 15%, test 15%

X_train_val, X_test, y_train_val, y_test = train_test_split(imdb_reduced['review'], imdb_reduced['sentiment'], test_size=0.15, random_state=42)

X_train, X_val, y_train, y_val = train_test_split(X_train_val, y_train_val, test_size=0.15, random_state=42)

#Feature extraction: Transforming data into TF-IDF features.
X_train = tfidf_vectorizer.fit_transform(X_train)
X_val = tfidf_vectorizer.transform(X_val)
X_test = tfidf_vectorizer.transform(X_test)

print(X_train.shape)  # Should output (number_of_samples, 33154)
print(X_test.shape)   # Should also output (number_of_samples, 33154)
print(X_val.shape)

(3612, 33154)
(750, 33154)
(638, 33154)

#Turning sparse matrix into dense
X_train = X_train.toarray()
X_val = X_val.toarray()
X_test = X_test.toarray()

#Turning into PyTorch tensors
X_train = torch.tensor(X_train, dtype=torch.float32)
X_val = torch.tensor(X_val, dtype=torch.float32)
X_test = torch.tensor(X_test, dtype=torch.float32)
y_train = torch.tensor(y_train.values, dtype=torch.float32)
y_val = torch.tensor(y_val.values, dtype=torch.float32)
y_test = torch.tensor(y_test.values, dtype=torch.float32)

#Resources:
# https://pytorch.org/docs/stable/tensors.html

#We need to know the shape of the input vector to set the in put dimenssion.
input_dim = X_train.shape[1] 
print(input_dim)

33154

# Time consumed (starts)
start_time = time.time()

#Building the Multilayer Perceptron model with back propagation.
class MLPmodel(nn.Module):
    def __init__(self):
        super(MLPmodel, self).__init__()
        self.fc1 = nn.Linear(33154,610)
        self.fc2 = nn.Linear(610,377)
        self.fc3 = nn.Linear(377,23)
        self.fc4 = nn.Linear(23,1)
        self.sigmoid = nn.Sigmoid()
        self.relu = nn.ReLU()
        
    def forward(self, x):
        hidden = self.relu(self.fc1(x))
        hidden = self.relu(self.fc2(hidden))
        hidden = self.relu(self.fc3(hidden))
        output = self.sigmoid(self.fc4(hidden))
        return output

# Adam optimizer
model = MLPmodel()
criterion = nn.BCELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.00001, weight_decay=0.001)  # Using Adam optimizer

epochs = 5000 # We limit the epochs to 5000 to limit the time spent
patience = 10 #Here we define how many epochs wait until we stop
best_val_loss = float('inf') #To save the best model/early stop
patience_counter = 0 #This one starts a counter to track number of epochs without improvement

for epoch in range(epochs):
    model.train()
    optimizer.zero_grad()
  #Forward Pass
    outputs = model(X_train)
    loss = criterion(outputs.squeeze(), y_train)
  #Backward and Optimize
    loss.backward()
    optimizer.step()

  #Validation
    model.eval()
    with torch.no_grad():
        val_outputs = model(X_val)
        val_loss = criterion(val_outputs.squeeze(), y_val)
       
  #Early stop
    if val_loss < best_val_loss:
        best_val_loss = val_loss
        best_model_state = model.state_dict()  # Save the best model state
        patience_counter=0
    else:
        patience_counter += 1
    
  #Print the early stopping
    if patience_counter > patience: 
        print(f'Stopping early at epoch {epoch+1}.')
        break
        
    if (epoch+1) % 5 == 0:
        print(f'Epoch {epoch+1}/{epochs}, Loss: {loss.item()}, Val Loss: {val_loss.item()}')
           
if best_model_state:
    model.load_state_dict(best_model_state)       
       
#Resources:
#https://pythonguides.com/pytorch-early-stopping/
#https://discuss.pytorch.org/t/can-i-deepcopy-a-model/52192
#https://machinelearningmastery.com/how-to-stop-training-deep-neural-networks-at-the-right-time-using-early-stopping/
#https://stackoverflow.com/questions/71998978/early-stopping-in-pytorch
#https://github.com/Bjarten/early-stopping-pytorch
#https://debuggercafe.com/using-learning-rate-scheduler-and-early-stopping-with-pytorch/

Epoch 5/5000, Loss: 0.6939665079116821, Val Loss: 0.6941571831703186
Epoch 10/5000, Loss: 0.6939529776573181, Val Loss: 0.6941444873809814
Epoch 15/5000, Loss: 0.6939393877983093, Val Loss: 0.6941317319869995
Epoch 20/5000, Loss: 0.6939254403114319, Val Loss: 0.6941187977790833
Epoch 25/5000, Loss: 0.6939113140106201, Val Loss: 0.6941055059432983
Epoch 30/5000, Loss: 0.6938966512680054, Val Loss: 0.6940917372703552
Epoch 35/5000, Loss: 0.6938818693161011, Val Loss: 0.6940780878067017
Epoch 40/5000, Loss: 0.6938672065734863, Val Loss: 0.694064736366272
Epoch 45/5000, Loss: 0.693854033946991, Val Loss: 0.6940527558326721
Epoch 50/5000, Loss: 0.6938410401344299, Val Loss: 0.6940410137176514
Epoch 55/5000, Loss: 0.6938280463218689, Val Loss: 0.6940295100212097
Epoch 60/5000, Loss: 0.6938151717185974, Val Loss: 0.6940178275108337
Epoch 65/5000, Loss: 0.6938018202781677, Val Loss: 0.6940059065818787
Epoch 70/5000, Loss: 0.693788468837738, Val Loss: 0.693993866443634
Epoch 75/5000, Loss: 0.6937750577926636, Val Loss: 0.6939818859100342
Epoch 80/5000, Loss: 0.6937615275382996, Val Loss: 0.6939697861671448
Epoch 85/5000, Loss: 0.6937476992607117, Val Loss: 0.6939573287963867
Epoch 90/5000, Loss: 0.693733811378479, Val Loss: 0.6939449906349182
Epoch 95/5000, Loss: 0.6937196254730225, Val Loss: 0.693932294845581
Epoch 100/5000, Loss: 0.6937052607536316, Val Loss: 0.6939194798469543
Epoch 105/5000, Loss: 0.6936905980110168, Val Loss: 0.6939064860343933
Epoch 110/5000, Loss: 0.6936756372451782, Val Loss: 0.693893313407898
Epoch 115/5000, Loss: 0.693660318851471, Val Loss: 0.6938799023628235
Epoch 120/5000, Loss: 0.6936447024345398, Val Loss: 0.6938663125038147
Epoch 125/5000, Loss: 0.6936288475990295, Val Loss: 0.6938523650169373
Epoch 130/5000, Loss: 0.6936125755310059, Val Loss: 0.6938381195068359
Epoch 135/5000, Loss: 0.6935958862304688, Val Loss: 0.6938235759735107
Epoch 140/5000, Loss: 0.6935787796974182, Val Loss: 0.6938087940216064
Epoch 145/5000, Loss: 0.6935611963272095, Val Loss: 0.6937934756278992
Epoch 150/5000, Loss: 0.6935431957244873, Val Loss: 0.693777859210968
Epoch 155/5000, Loss: 0.6935246586799622, Val Loss: 0.6937617063522339
Epoch 160/5000, Loss: 0.6935054659843445, Val Loss: 0.6937452554702759
Epoch 165/5000, Loss: 0.6934857964515686, Val Loss: 0.6937282085418701
Epoch 170/5000, Loss: 0.6934654712677002, Val Loss: 0.6937109231948853
Epoch 175/5000, Loss: 0.6934447288513184, Val Loss: 0.6936932802200317
Epoch 180/5000, Loss: 0.6934233903884888, Val Loss: 0.6936752200126648
Epoch 185/5000, Loss: 0.693401575088501, Val Loss: 0.6936569213867188
Epoch 190/5000, Loss: 0.6933791041374207, Val Loss: 0.6936381459236145
Epoch 195/5000, Loss: 0.6933561563491821, Val Loss: 0.6936187744140625
Epoch 200/5000, Loss: 0.6933324933052063, Val Loss: 0.6935989856719971
Epoch 205/5000, Loss: 0.6933079957962036, Val Loss: 0.6935785412788391
Epoch 210/5000, Loss: 0.6932826638221741, Val Loss: 0.6935572624206543
Epoch 215/5000, Loss: 0.693256139755249, Val Loss: 0.6935352087020874
Epoch 220/5000, Loss: 0.6932287216186523, Val Loss: 0.6935124397277832
Epoch 225/5000, Loss: 0.6932005882263184, Val Loss: 0.6934893131256104
Epoch 230/5000, Loss: 0.6931718587875366, Val Loss: 0.6934658288955688
Epoch 235/5000, Loss: 0.6931424736976624, Val Loss: 0.6934418678283691
Epoch 240/5000, Loss: 0.6931123733520508, Val Loss: 0.6934173703193665
Epoch 245/5000, Loss: 0.6930816173553467, Val Loss: 0.6933923959732056
Epoch 250/5000, Loss: 0.693049967288971, Val Loss: 0.6933667659759521
Epoch 255/5000, Loss: 0.6930175423622131, Val Loss: 0.6933403611183167
Epoch 260/5000, Loss: 0.6929842233657837, Val Loss: 0.6933134198188782
Epoch 265/5000, Loss: 0.6929498314857483, Val Loss: 0.6932856440544128
Epoch 270/5000, Loss: 0.6929145455360413, Val Loss: 0.6932571530342102
Epoch 275/5000, Loss: 0.6928783059120178, Val Loss: 0.693228006362915
Epoch 280/5000, Loss: 0.692841112613678, Val Loss: 0.6931981444358826
Epoch 285/5000, Loss: 0.6928026676177979, Val Loss: 0.6931674480438232
Epoch 290/5000, Loss: 0.6927632093429565, Val Loss: 0.6931360960006714
Epoch 295/5000, Loss: 0.6927226185798645, Val Loss: 0.6931037306785583
Epoch 300/5000, Loss: 0.6926807761192322, Val Loss: 0.6930708289146423
Epoch 305/5000, Loss: 0.6926378607749939, Val Loss: 0.6930368542671204
Epoch 310/5000, Loss: 0.6925936937332153, Val Loss: 0.6930020451545715
Epoch 315/5000, Loss: 0.692548394203186, Val Loss: 0.6929664611816406
Epoch 320/5000, Loss: 0.6925018429756165, Val Loss: 0.6929299235343933
Epoch 325/5000, Loss: 0.6924539804458618, Val Loss: 0.6928924918174744
Epoch 330/5000, Loss: 0.6924046277999878, Val Loss: 0.6928539872169495
Epoch 335/5000, Loss: 0.6923540234565735, Val Loss: 0.6928144097328186
Epoch 340/5000, Loss: 0.6923019289970398, Val Loss: 0.6927738785743713
Epoch 345/5000, Loss: 0.6922481060028076, Val Loss: 0.6927322149276733
Epoch 350/5000, Loss: 0.6921926140785217, Val Loss: 0.6926892399787903
Epoch 355/5000, Loss: 0.6921353340148926, Val Loss: 0.6926448941230774
Epoch 360/5000, Loss: 0.6920759081840515, Val Loss: 0.6925989985466003
Epoch 365/5000, Loss: 0.6920140981674194, Val Loss: 0.6925513744354248
Epoch 370/5000, Loss: 0.6919504404067993, Val Loss: 0.6925027966499329
Epoch 375/5000, Loss: 0.6918853521347046, Val Loss: 0.6924528479576111
Epoch 380/5000, Loss: 0.6918180584907532, Val Loss: 0.6924015283584595
Epoch 385/5000, Loss: 0.6917487978935242, Val Loss: 0.6923485398292542
Epoch 390/5000, Loss: 0.6916771531105042, Val Loss: 0.6922940611839294
Epoch 395/5000, Loss: 0.6916031241416931, Val Loss: 0.6922378540039062
Epoch 400/5000, Loss: 0.6915267109870911, Val Loss: 0.692179799079895
Epoch 405/5000, Loss: 0.6914476752281189, Val Loss: 0.6921198964118958
Epoch 410/5000, Loss: 0.6913660764694214, Val Loss: 0.6920581459999084
Epoch 415/5000, Loss: 0.6912816166877747, Val Loss: 0.6919944882392883
Epoch 420/5000, Loss: 0.6911945343017578, Val Loss: 0.6919288039207458
Epoch 425/5000, Loss: 0.6911044716835022, Val Loss: 0.691861093044281
Epoch 430/5000, Loss: 0.6910114884376526, Val Loss: 0.6917914152145386
Epoch 435/5000, Loss: 0.6909155249595642, Val Loss: 0.6917195916175842
Epoch 440/5000, Loss: 0.6908164620399475, Val Loss: 0.6916458606719971
Epoch 445/5000, Loss: 0.6907138824462891, Val Loss: 0.6915698051452637
Epoch 450/5000, Loss: 0.6906076669692993, Val Loss: 0.6914912462234497
Epoch 455/5000, Loss: 0.6904975771903992, Val Loss: 0.691410481929779
Epoch 460/5000, Loss: 0.6903842687606812, Val Loss: 0.6913273930549622
Epoch 465/5000, Loss: 0.6902672052383423, Val Loss: 0.6912416815757751
Epoch 470/5000, Loss: 0.6901462078094482, Val Loss: 0.6911531686782837
Epoch 475/5000, Loss: 0.6900211572647095, Val Loss: 0.691061794757843
Epoch 480/5000, Loss: 0.6898917555809021, Val Loss: 0.6909673810005188
Epoch 485/5000, Loss: 0.6897577047348022, Val Loss: 0.6908696293830872
Epoch 490/5000, Loss: 0.6896188855171204, Val Loss: 0.6907686591148376
Epoch 495/5000, Loss: 0.6894751787185669, Val Loss: 0.6906639933586121
Epoch 500/5000, Loss: 0.6893259286880493, Val Loss: 0.6905557513237
Epoch 505/5000, Loss: 0.6891709566116333, Val Loss: 0.6904433369636536
Epoch 510/5000, Loss: 0.6890096664428711, Val Loss: 0.6903260946273804
Epoch 515/5000, Loss: 0.6888407468795776, Val Loss: 0.6902033090591431
Epoch 520/5000, Loss: 0.688663899898529, Val Loss: 0.6900754570960999
Epoch 525/5000, Loss: 0.6884815692901611, Val Loss: 0.6899445652961731
Epoch 530/5000, Loss: 0.6882947683334351, Val Loss: 0.6898109912872314
Epoch 535/5000, Loss: 0.6881021857261658, Val Loss: 0.6896730661392212
Epoch 540/5000, Loss: 0.6879026293754578, Val Loss: 0.6895301342010498
Epoch 545/5000, Loss: 0.6876956820487976, Val Loss: 0.689382016658783
Epoch 550/5000, Loss: 0.6874813437461853, Val Loss: 0.6892287135124207
Epoch 555/5000, Loss: 0.6872594952583313, Val Loss: 0.689070463180542
Epoch 560/5000, Loss: 0.6870300769805908, Val Loss: 0.6889069676399231
Epoch 565/5000, Loss: 0.6867927312850952, Val Loss: 0.6887379288673401
Epoch 570/5000, Loss: 0.6865469813346863, Val Loss: 0.6885634064674377
Epoch 575/5000, Loss: 0.6862930059432983, Val Loss: 0.6883832216262817
Epoch 580/5000, Loss: 0.6860305070877075, Val Loss: 0.6881972551345825
Epoch 585/5000, Loss: 0.6857591867446899, Val Loss: 0.6880053877830505

Epoch 590/5000, Loss: 0.685478925704956, Val Loss: 0.6878074407577515
Epoch 595/5000, Loss: 0.6851899027824402, Val Loss: 0.6876032948493958
Epoch 600/5000, Loss: 0.684891939163208, Val Loss: 0.6873931288719177
Epoch 605/5000, Loss: 0.6845850348472595, Val Loss: 0.6871773600578308
Epoch 610/5000, Loss: 0.6842691898345947, Val Loss: 0.6869557499885559
Epoch 615/5000, Loss: 0.6839444041252136, Val Loss: 0.686728298664093
Epoch 620/5000, Loss: 0.6836093664169312, Val Loss: 0.6864942312240601
Epoch 625/5000, Loss: 0.683264434337616, Val Loss: 0.6862534284591675
Epoch 630/5000, Loss: 0.6829090714454651, Val Loss: 0.6860058307647705
Epoch 635/5000, Loss: 0.6825433969497681, Val Loss: 0.6857519149780273
Epoch 640/5000, Loss: 0.6821677684783936, Val Loss: 0.6854922771453857
Epoch 645/5000, Loss: 0.6817816495895386, Val Loss: 0.6852260828018188
Epoch 650/5000, Loss: 0.681384265422821, Val Loss: 0.6849525570869446
Epoch 655/5000, Loss: 0.6809757351875305, Val Loss: 0.6846722364425659
Epoch 660/5000, Loss: 0.6805568337440491, Val Loss: 0.6843854188919067
Epoch 665/5000, Loss: 0.6801274418830872, Val Loss: 0.6840918064117432
Epoch 670/5000, Loss: 0.6796873807907104, Val Loss: 0.6837911605834961
Epoch 675/5000, Loss: 0.679236114025116, Val Loss: 0.6834836602210999
Epoch 680/5000, Loss: 0.6787740588188171, Val Loss: 0.6831695437431335
Epoch 685/5000, Loss: 0.6783016920089722, Val Loss: 0.6828492879867554
Epoch 690/5000, Loss: 0.677819013595581, Val Loss: 0.6825224757194519
Epoch 695/5000, Loss: 0.6773268580436707, Val Loss: 0.6821902394294739
Epoch 700/5000, Loss: 0.6768255233764648, Val Loss: 0.6818523406982422
Epoch 705/5000, Loss: 0.6763145327568054, Val Loss: 0.6815087199211121
Epoch 710/5000, Loss: 0.6757943630218506, Val Loss: 0.681159257888794
Epoch 715/5000, Loss: 0.6752650141716003, Val Loss: 0.680804967880249
Epoch 720/5000, Loss: 0.6747264862060547, Val Loss: 0.6804453134536743
Epoch 725/5000, Loss: 0.6741785407066345, Val Loss: 0.680079996585846
Epoch 730/5000, Loss: 0.6736204624176025, Val Loss: 0.6797086596488953
Epoch 735/5000, Loss: 0.6730521321296692, Val Loss: 0.6793314218521118
Epoch 740/5000, Loss: 0.6724729537963867, Val Loss: 0.678948163986206
Epoch 745/5000, Loss: 0.671883761882782, Val Loss: 0.6785585284233093
Epoch 750/5000, Loss: 0.6712839603424072, Val Loss: 0.678162157535553
Epoch 755/5000, Loss: 0.6706728935241699, Val Loss: 0.6777591705322266
Epoch 760/5000, Loss: 0.6700509786605835, Val Loss: 0.6773497462272644
Epoch 765/5000, Loss: 0.6694175601005554, Val Loss: 0.6769329905509949
Epoch 770/5000, Loss: 0.6687713265419006, Val Loss: 0.6765075325965881
Epoch 775/5000, Loss: 0.6681123375892639, Val Loss: 0.6760738492012024
Epoch 780/5000, Loss: 0.6674399971961975, Val Loss: 0.6756312847137451
Epoch 785/5000, Loss: 0.6667523980140686, Val Loss: 0.6751782894134521
Epoch 790/5000, Loss: 0.6660449504852295, Val Loss: 0.674709677696228
Epoch 795/5000, Loss: 0.6653085947036743, Val Loss: 0.6742164492607117
Epoch 800/5000, Loss: 0.6645296216011047, Val Loss: 0.6736953854560852
Epoch 805/5000, Loss: 0.6636993885040283, Val Loss: 0.673137903213501
Epoch 810/5000, Loss: 0.6628208160400391, Val Loss: 0.6725438237190247
Epoch 815/5000, Loss: 0.661911129951477, Val Loss: 0.6719339489936829
Epoch 820/5000, Loss: 0.6609910130500793, Val Loss: 0.6713282465934753
Epoch 825/5000, Loss: 0.660071074962616, Val Loss: 0.6707318425178528
Epoch 830/5000, Loss: 0.6591505408287048, Val Loss: 0.6701388955116272
Epoch 835/5000, Loss: 0.6582183837890625, Val Loss: 0.6695374846458435
Epoch 840/5000, Loss: 0.6572701334953308, Val Loss: 0.6689239740371704
Epoch 845/5000, Loss: 0.6563036441802979, Val Loss: 0.6682963967323303
Epoch 850/5000, Loss: 0.6553184390068054, Val Loss: 0.6676558256149292
Epoch 855/5000, Loss: 0.6543151140213013, Val Loss: 0.667003333568573
Epoch 860/5000, Loss: 0.6532918810844421, Val Loss: 0.6663392186164856
Epoch 865/5000, Loss: 0.65224689245224, Val Loss: 0.6656623482704163
Epoch 870/5000, Loss: 0.6511792540550232, Val Loss: 0.6649711728096008
Epoch 875/5000, Loss: 0.6500898599624634, Val Loss: 0.6642676591873169
Epoch 880/5000, Loss: 0.648979127407074, Val Loss: 0.663550615310669
Epoch 885/5000, Loss: 0.6478453874588013, Val Loss: 0.6628183126449585
Epoch 890/5000, Loss: 0.6466871500015259, Val Loss: 0.6620697975158691
Epoch 895/5000, Loss: 0.6455037593841553, Val Loss: 0.6613061428070068
Epoch 900/5000, Loss: 0.644294798374176, Val Loss: 0.6605260372161865
Epoch 905/5000, Loss: 0.643059253692627, Val Loss: 0.6597300171852112
Epoch 910/5000, Loss: 0.6417972445487976, Val Loss: 0.6589182615280151
Epoch 915/5000, Loss: 0.6405082941055298, Val Loss: 0.658088743686676
Epoch 920/5000, Loss: 0.639193058013916, Val Loss: 0.657241940498352
Epoch 925/5000, Loss: 0.6378501057624817, Val Loss: 0.6563780307769775
Epoch 930/5000, Loss: 0.6364782452583313, Val Loss: 0.6554962992668152
Epoch 935/5000, Loss: 0.6350770592689514, Val Loss: 0.6545971035957336
Epoch 940/5000, Loss: 0.6336454153060913, Val Loss: 0.6536787748336792
Epoch 945/5000, Loss: 0.6321806311607361, Val Loss: 0.6527405381202698
Epoch 950/5000, Loss: 0.6306812167167664, Val Loss: 0.6517813801765442
Epoch 955/5000, Loss: 0.6291509866714478, Val Loss: 0.6508057117462158
Epoch 960/5000, Loss: 0.6275933980941772, Val Loss: 0.6498135328292847
Epoch 965/5000, Loss: 0.626004159450531, Val Loss: 0.6488023996353149
Epoch 970/5000, Loss: 0.6243816018104553, Val Loss: 0.647771954536438
Epoch 975/5000, Loss: 0.6227269172668457, Val Loss: 0.6467223167419434
Epoch 980/5000, Loss: 0.621039628982544, Val Loss: 0.6456530690193176
Epoch 985/5000, Loss: 0.619319498538971, Val Loss: 0.6445640921592712
Epoch 990/5000, Loss: 0.6175658702850342, Val Loss: 0.6434552669525146
Epoch 995/5000, Loss: 0.6157785058021545, Val Loss: 0.6423264145851135
Epoch 1000/5000, Loss: 0.6139562726020813, Val Loss: 0.6411778330802917
Epoch 1005/5000, Loss: 0.6121004223823547, Val Loss: 0.6400091052055359
Epoch 1010/5000, Loss: 0.6102099418640137, Val Loss: 0.6388205885887146
Epoch 1015/5000, Loss: 0.6082854866981506, Val Loss: 0.6376123428344727
Epoch 1020/5000, Loss: 0.606326699256897, Val Loss: 0.6363853812217712
Epoch 1025/5000, Loss: 0.6043332815170288, Val Loss: 0.6351380348205566
Epoch 1030/5000, Loss: 0.602304995059967, Val Loss: 0.6338715553283691
Epoch 1035/5000, Loss: 0.6002417206764221, Val Loss: 0.6325849890708923
Epoch 1040/5000, Loss: 0.5981440544128418, Val Loss: 0.631279706954956
Epoch 1045/5000, Loss: 0.5960116982460022, Val Loss: 0.6299546957015991
Epoch 1050/5000, Loss: 0.5938453078269958, Val Loss: 0.6286099553108215
Epoch 1055/5000, Loss: 0.591645359992981, Val Loss: 0.6272468566894531
Epoch 1060/5000, Loss: 0.5894114971160889, Val Loss: 0.6258639693260193
Epoch 1065/5000, Loss: 0.5871431827545166, Val Loss: 0.6244609951972961
Epoch 1070/5000, Loss: 0.584840714931488, Val Loss: 0.6230393052101135
Epoch 1075/5000, Loss: 0.5825036764144897, Val Loss: 0.6215986013412476
Epoch 1080/5000, Loss: 0.5801335573196411, Val Loss: 0.6201401352882385
Epoch 1085/5000, Loss: 0.5777297019958496, Val Loss: 0.6186639070510864
Epoch 1090/5000, Loss: 0.5752930641174316, Val Loss: 0.6171682476997375
Epoch 1095/5000, Loss: 0.5728220343589783, Val Loss: 0.6156566739082336
Epoch 1100/5000, Loss: 0.5703152418136597, Val Loss: 0.614124059677124
Epoch 1105/5000, Loss: 0.5677748918533325, Val Loss: 0.6125760674476624
Epoch 1110/5000, Loss: 0.5652017593383789, Val Loss: 0.611009955406189
Epoch 1115/5000, Loss: 0.5625969171524048, Val Loss: 0.6094270944595337
Epoch 1120/5000, Loss: 0.5599607825279236, Val Loss: 0.6078291535377502
Epoch 1125/5000, Loss: 0.5572932958602905, Val Loss: 0.6062130331993103
Epoch 1130/5000, Loss: 0.554595410823822, Val Loss: 0.6045799255371094
Epoch 1135/5000, Loss: 0.5518658757209778, Val Loss: 0.6029308438301086
Epoch 1140/5000, Loss: 0.5491039752960205, Val Loss: 0.6012644171714783
Epoch 1145/5000, Loss: 0.5463094115257263, Val Loss: 0.5995804071426392
Epoch 1150/5000, Loss: 0.5434820652008057, Val Loss: 0.59787917137146
Epoch 1155/5000, Loss: 0.540619432926178, Val Loss: 0.5961612462997437
Epoch 1160/5000, Loss: 0.5377176403999329, Val Loss: 0.5944254994392395
Epoch 1165/5000, Loss: 0.534763753414154, Val Loss: 0.592668354511261

Epoch 1170/5000, Loss: 0.5317560434341431, Val Loss: 0.5908834934234619
Epoch 1175/5000, Loss: 0.5287042260169983, Val Loss: 0.5890734195709229
Epoch 1180/5000, Loss: 0.5256170630455017, Val Loss: 0.587239682674408
Epoch 1185/5000, Loss: 0.5224999785423279, Val Loss: 0.5853869915008545
Epoch 1190/5000, Loss: 0.5193573832511902, Val Loss: 0.5835187435150146
Epoch 1195/5000, Loss: 0.5161933898925781, Val Loss: 0.5816386342048645
Epoch 1200/5000, Loss: 0.5130094289779663, Val Loss: 0.5797520279884338
Epoch 1205/5000, Loss: 0.5098110437393188, Val Loss: 0.5778675079345703
Epoch 1210/5000, Loss: 0.5066003799438477, Val Loss: 0.5759832859039307
Epoch 1215/5000, Loss: 0.503379762172699, Val Loss: 0.5740995407104492
Epoch 1220/5000, Loss: 0.500148594379425, Val Loss: 0.5722125172615051
Epoch 1225/5000, Loss: 0.496906042098999, Val Loss: 0.5703213214874268
Epoch 1230/5000, Loss: 0.49365130066871643, Val Loss: 0.5684238076210022
Epoch 1235/5000, Loss: 0.49038517475128174, Val Loss: 0.5665238499641418
Epoch 1240/5000, Loss: 0.48710766434669495, Val Loss: 0.5646191239356995
Epoch 1245/5000, Loss: 0.4838203489780426, Val Loss: 0.5627126693725586
Epoch 1250/5000, Loss: 0.4805234968662262, Val Loss: 0.560802698135376
Epoch 1255/5000, Loss: 0.4772186577320099, Val Loss: 0.5588908195495605
Epoch 1260/5000, Loss: 0.47390663623809814, Val Loss: 0.556977391242981
Epoch 1265/5000, Loss: 0.47058767080307007, Val Loss: 0.5550618171691895
Epoch 1270/5000, Loss: 0.46726229786872864, Val Loss: 0.5531455278396606
Epoch 1275/5000, Loss: 0.46393340826034546, Val Loss: 0.5512285828590393
Epoch 1280/5000, Loss: 0.4606002867221832, Val Loss: 0.5493136048316956
Epoch 1285/5000, Loss: 0.45726510882377625, Val Loss: 0.5473980903625488
Epoch 1290/5000, Loss: 0.4539282023906708, Val Loss: 0.5454854965209961
Epoch 1295/5000, Loss: 0.4505890905857086, Val Loss: 0.5435720682144165
Epoch 1300/5000, Loss: 0.4472494423389435, Val Loss: 0.5416629314422607
Epoch 1305/5000, Loss: 0.4439091086387634, Val Loss: 0.53975510597229
Epoch 1310/5000, Loss: 0.44056984782218933, Val Loss: 0.5378463268280029
Epoch 1315/5000, Loss: 0.43723466992378235, Val Loss: 0.5359472632408142
Epoch 1320/5000, Loss: 0.43390312790870667, Val Loss: 0.534050464630127
Epoch 1325/5000, Loss: 0.4305756986141205, Val Loss: 0.53216153383255
Epoch 1330/5000, Loss: 0.42725351452827454, Val Loss: 0.5302768349647522
Epoch 1335/5000, Loss: 0.4239383339881897, Val Loss: 0.5283970832824707
Epoch 1340/5000, Loss: 0.42063039541244507, Val Loss: 0.5265271067619324
Epoch 1345/5000, Loss: 0.4173309803009033, Val Loss: 0.5246638655662537
Epoch 1350/5000, Loss: 0.41404134035110474, Val Loss: 0.5228095054626465
Epoch 1355/5000, Loss: 0.4107629060745239, Val Loss: 0.5209620594978333
Epoch 1360/5000, Loss: 0.4074963331222534, Val Loss: 0.5191252827644348
Epoch 1365/5000, Loss: 0.40424275398254395, Val Loss: 0.5172966718673706
Epoch 1370/5000, Loss: 0.40100178122520447, Val Loss: 0.5154774785041809
Epoch 1375/5000, Loss: 0.39777466654777527, Val Loss: 0.5136670470237732
Epoch 1380/5000, Loss: 0.3945622742176056, Val Loss: 0.511863112449646
Epoch 1385/5000, Loss: 0.39136430621147156, Val Loss: 0.5100699663162231
Epoch 1390/5000, Loss: 0.3881811499595642, Val Loss: 0.5082842111587524
Epoch 1395/5000, Loss: 0.3850144147872925, Val Loss: 0.5065058469772339
Epoch 1400/5000, Loss: 0.38186347484588623, Val Loss: 0.5047376155853271
Epoch 1405/5000, Loss: 0.37873002886772156, Val Loss: 0.5029829740524292
Epoch 1410/5000, Loss: 0.3756137192249298, Val Loss: 0.5012344717979431
Epoch 1415/5000, Loss: 0.3725123405456543, Val Loss: 0.4995003938674927
Epoch 1420/5000, Loss: 0.3694271445274353, Val Loss: 0.4977760910987854
Epoch 1425/5000, Loss: 0.36636051535606384, Val Loss: 0.49606528878211975
Epoch 1430/5000, Loss: 0.3633137345314026, Val Loss: 0.49436521530151367
Epoch 1435/5000, Loss: 0.36028775572776794, Val Loss: 0.4926760196685791
Epoch 1440/5000, Loss: 0.35728299617767334, Val Loss: 0.4910012483596802
Epoch 1445/5000, Loss: 0.35430070757865906, Val Loss: 0.4893389344215393
Epoch 1450/5000, Loss: 0.35134074091911316, Val Loss: 0.48768845200538635
Epoch 1455/5000, Loss: 0.348404198884964, Val Loss: 0.4860523045063019
Epoch 1460/5000, Loss: 0.3454890251159668, Val Loss: 0.48442542552948
Epoch 1465/5000, Loss: 0.3425975441932678, Val Loss: 0.4828133285045624
Epoch 1470/5000, Loss: 0.3397289514541626, Val Loss: 0.4812159538269043
Epoch 1475/5000, Loss: 0.3368838429450989, Val Loss: 0.47962990403175354
Epoch 1480/5000, Loss: 0.3340611159801483, Val Loss: 0.478059858083725
Epoch 1485/5000, Loss: 0.33126312494277954, Val Loss: 0.47650060057640076
Epoch 1490/5000, Loss: 0.32848936319351196, Val Loss: 0.47495803236961365
Epoch 1495/5000, Loss: 0.325740784406662, Val Loss: 0.47342947125434875
Epoch 1500/5000, Loss: 0.32301661372184753, Val Loss: 0.4719167947769165
Epoch 1505/5000, Loss: 0.32031726837158203, Val Loss: 0.4704166054725647
Epoch 1510/5000, Loss: 0.31764164566993713, Val Loss: 0.46892908215522766
Epoch 1515/5000, Loss: 0.3149908781051636, Val Loss: 0.4674568176269531
Epoch 1520/5000, Loss: 0.31236425042152405, Val Loss: 0.4660016894340515
Epoch 1525/5000, Loss: 0.3097634017467499, Val Loss: 0.4645580053329468
Epoch 1530/5000, Loss: 0.3071875274181366, Val Loss: 0.46312710642814636
Epoch 1535/5000, Loss: 0.3046362102031708, Val Loss: 0.4617105722427368
Epoch 1540/5000, Loss: 0.30210965871810913, Val Loss: 0.4603140354156494
Epoch 1545/5000, Loss: 0.2996077239513397, Val Loss: 0.458926260471344
Epoch 1550/5000, Loss: 0.29712989926338196, Val Loss: 0.4575567841529846
Epoch 1555/5000, Loss: 0.2946772873401642, Val Loss: 0.4561988115310669
Epoch 1560/5000, Loss: 0.2922491729259491, Val Loss: 0.4548569917678833
Epoch 1565/5000, Loss: 0.2898465394973755, Val Loss: 0.4535275995731354
Epoch 1570/5000, Loss: 0.2874682545661926, Val Loss: 0.45221400260925293
Epoch 1575/5000, Loss: 0.2851148247718811, Val Loss: 0.45091184973716736
Epoch 1580/5000, Loss: 0.28278473019599915, Val Loss: 0.4496247172355652
Epoch 1585/5000, Loss: 0.2804795205593109, Val Loss: 0.4483506679534912
Epoch 1590/5000, Loss: 0.2781982719898224, Val Loss: 0.4470934271812439
Epoch 1595/5000, Loss: 0.2759407162666321, Val Loss: 0.44584569334983826
Epoch 1600/5000, Loss: 0.2737085819244385, Val Loss: 0.4446153938770294
Epoch 1605/5000, Loss: 0.2714989185333252, Val Loss: 0.4433976411819458
Epoch 1610/5000, Loss: 0.26931408047676086, Val Loss: 0.44218909740448
Epoch 1615/5000, Loss: 0.26715266704559326, Val Loss: 0.4409966468811035
Epoch 1620/5000, Loss: 0.26501426100730896, Val Loss: 0.4398147463798523
Epoch 1625/5000, Loss: 0.26289936900138855, Val Loss: 0.4386487603187561
Epoch 1630/5000, Loss: 0.26080748438835144, Val Loss: 0.43749314546585083
Epoch 1635/5000, Loss: 0.25873881578445435, Val Loss: 0.43635129928588867
Epoch 1640/5000, Loss: 0.25669237971305847, Val Loss: 0.4352240562438965
Epoch 1645/5000, Loss: 0.2546692192554474, Val Loss: 0.4341089725494385
Epoch 1650/5000, Loss: 0.25266799330711365, Val Loss: 0.4330037832260132
Epoch 1655/5000, Loss: 0.25068938732147217, Val Loss: 0.4319118857383728
Epoch 1660/5000, Loss: 0.24873222410678864, Val Loss: 0.4308345317840576
Epoch 1665/5000, Loss: 0.2467978298664093, Val Loss: 0.42976877093315125
Epoch 1670/5000, Loss: 0.24488461017608643, Val Loss: 0.42871537804603577
Epoch 1675/5000, Loss: 0.24299225211143494, Val Loss: 0.42767563462257385
Epoch 1680/5000, Loss: 0.24112015962600708, Val Loss: 0.4266500473022461
Epoch 1685/5000, Loss: 0.23926839232444763, Val Loss: 0.42563319206237793
Epoch 1690/5000, Loss: 0.2374381124973297, Val Loss: 0.42462998628616333
Epoch 1695/5000, Loss: 0.23562733829021454, Val Loss: 0.4236351549625397
Epoch 1700/5000, Loss: 0.2338370531797409, Val Loss: 0.4226550757884979
Epoch 1705/5000, Loss: 0.23206713795661926, Val Loss: 0.4216836094856262
Epoch 1710/5000, Loss: 0.23031745851039886, Val Loss: 0.4207267761230469
Epoch 1715/5000, Loss: 0.22858789563179016, Val Loss: 0.4197801351547241
Epoch 1720/5000, Loss: 0.2268780618906021, Val Loss: 0.418840616941452
Epoch 1725/5000, Loss: 0.22518786787986755, Val Loss: 0.4179174602031708
Epoch 1730/5000, Loss: 0.2235163450241089, Val Loss: 0.417002409696579
Epoch 1735/5000, Loss: 0.22186428308486938, Val Loss: 0.4161013066768646

Epoch 1740/5000, Loss: 0.22023025155067444, Val Loss: 0.4152122139930725
Epoch 1745/5000, Loss: 0.21861547231674194, Val Loss: 0.4143317937850952
Epoch 1750/5000, Loss: 0.21701793372631073, Val Loss: 0.4134610593318939
Epoch 1755/5000, Loss: 0.21543781459331512, Val Loss: 0.412603497505188
Epoch 1760/5000, Loss: 0.21387597918510437, Val Loss: 0.41175177693367004
Epoch 1765/5000, Loss: 0.2123311311006546, Val Loss: 0.4109156131744385
Epoch 1770/5000, Loss: 0.2108050137758255, Val Loss: 0.410086065530777
Epoch 1775/5000, Loss: 0.20929498970508575, Val Loss: 0.4092668294906616
Epoch 1780/5000, Loss: 0.2078021764755249, Val Loss: 0.408457487821579
Epoch 1785/5000, Loss: 0.20632733404636383, Val Loss: 0.40765735507011414
Epoch 1790/5000, Loss: 0.2048679143190384, Val Loss: 0.4068678021430969
Epoch 1795/5000, Loss: 0.20342551171779633, Val Loss: 0.40608498454093933
Epoch 1800/5000, Loss: 0.20199912786483765, Val Loss: 0.4053140878677368
Epoch 1805/5000, Loss: 0.2005891650915146, Val Loss: 0.4045546352863312
Epoch 1810/5000, Loss: 0.19919468462467194, Val Loss: 0.4037986993789673
Epoch 1815/5000, Loss: 0.19781619310379028, Val Loss: 0.4030524492263794
Epoch 1820/5000, Loss: 0.19645212590694427, Val Loss: 0.40231791138648987
Epoch 1825/5000, Loss: 0.19510425627231598, Val Loss: 0.4015866816043854
Epoch 1830/5000, Loss: 0.19377073645591736, Val Loss: 0.40086647868156433
Epoch 1835/5000, Loss: 0.19245216250419617, Val Loss: 0.4001540243625641
Epoch 1840/5000, Loss: 0.19114802777767181, Val Loss: 0.39945143461227417
Epoch 1845/5000, Loss: 0.1898585557937622, Val Loss: 0.3987537920475006
Epoch 1850/5000, Loss: 0.18858227133750916, Val Loss: 0.39806753396987915
Epoch 1855/5000, Loss: 0.1873207986354828, Val Loss: 0.39738956093788147
Epoch 1860/5000, Loss: 0.18607300519943237, Val Loss: 0.39672011137008667
Epoch 1865/5000, Loss: 0.1848384439945221, Val Loss: 0.39605769515037537
Epoch 1870/5000, Loss: 0.18361824750900269, Val Loss: 0.3954015374183655
Epoch 1875/5000, Loss: 0.1824115365743637, Val Loss: 0.3947529196739197
Epoch 1880/5000, Loss: 0.18121640384197235, Val Loss: 0.39411160349845886
Epoch 1885/5000, Loss: 0.18003256618976593, Val Loss: 0.3934754431247711
Epoch 1890/5000, Loss: 0.17886392772197723, Val Loss: 0.3928494453430176
Epoch 1895/5000, Loss: 0.17770765721797943, Val Loss: 0.39223161339759827
Epoch 1900/5000, Loss: 0.17656347155570984, Val Loss: 0.3916175365447998
Epoch 1905/5000, Loss: 0.17543138563632965, Val Loss: 0.3910129964351654
Epoch 1910/5000, Loss: 0.1743118166923523, Val Loss: 0.39041581749916077
Epoch 1915/5000, Loss: 0.1732034981250763, Val Loss: 0.3898216485977173
Epoch 1920/5000, Loss: 0.1721067726612091, Val Loss: 0.38923758268356323
Epoch 1925/5000, Loss: 0.17102216184139252, Val Loss: 0.38866177201271057
Epoch 1930/5000, Loss: 0.16994935274124146, Val Loss: 0.3880860507488251
Epoch 1935/5000, Loss: 0.16888748109340668, Val Loss: 0.387520432472229
Epoch 1940/5000, Loss: 0.16783645749092102, Val Loss: 0.3869617283344269
Epoch 1945/5000, Loss: 0.1667967587709427, Val Loss: 0.3864125907421112
Epoch 1950/5000, Loss: 0.16576822102069855, Val Loss: 0.3858667016029358
Epoch 1955/5000, Loss: 0.16474997997283936, Val Loss: 0.3853283226490021
Epoch 1960/5000, Loss: 0.16374342143535614, Val Loss: 0.3847925066947937
Epoch 1965/5000, Loss: 0.16274619102478027, Val Loss: 0.38426557183265686
Epoch 1970/5000, Loss: 0.1617593914270401, Val Loss: 0.3837445080280304
Epoch 1975/5000, Loss: 0.1607830673456192, Val Loss: 0.38323280215263367
Epoch 1980/5000, Loss: 0.1598171591758728, Val Loss: 0.3827201724052429
Epoch 1985/5000, Loss: 0.15886054933071136, Val Loss: 0.38221660256385803
Epoch 1990/5000, Loss: 0.15791334211826324, Val Loss: 0.3817192614078522
Epoch 1995/5000, Loss: 0.15697568655014038, Val Loss: 0.3812289237976074
Epoch 2000/5000, Loss: 0.156047984957695, Val Loss: 0.3807409107685089
Epoch 2005/5000, Loss: 0.1551302671432495, Val Loss: 0.38025930523872375
Epoch 2010/5000, Loss: 0.15422150492668152, Val Loss: 0.37978073954582214
Epoch 2015/5000, Loss: 0.15332205593585968, Val Loss: 0.37931016087532043
Epoch 2020/5000, Loss: 0.15243130922317505, Val Loss: 0.3788429796695709
Epoch 2025/5000, Loss: 0.15154942870140076, Val Loss: 0.3783791661262512
Epoch 2030/5000, Loss: 0.150676891207695, Val Loss: 0.37792298197746277
Epoch 2035/5000, Loss: 0.14981213212013245, Val Loss: 0.3774694502353668
Epoch 2040/5000, Loss: 0.1489565223455429, Val Loss: 0.37702178955078125
Epoch 2045/5000, Loss: 0.14810891449451447, Val Loss: 0.3765776455402374
Epoch 2050/5000, Loss: 0.14726997911930084, Val Loss: 0.37614157795906067
Epoch 2055/5000, Loss: 0.14643874764442444, Val Loss: 0.37570834159851074
Epoch 2060/5000, Loss: 0.14561505615711212, Val Loss: 0.3752814829349518
Epoch 2065/5000, Loss: 0.14477509260177612, Val Loss: 0.3748369514942169
Epoch 2070/5000, Loss: 0.14391161501407623, Val Loss: 0.37438055872917175
Epoch 2075/5000, Loss: 0.1430514007806778, Val Loss: 0.37392905354499817
Epoch 2080/5000, Loss: 0.1421993523836136, Val Loss: 0.37348636984825134
Epoch 2085/5000, Loss: 0.14135777950286865, Val Loss: 0.3730517029762268
Epoch 2090/5000, Loss: 0.14052782952785492, Val Loss: 0.37262779474258423
Epoch 2095/5000, Loss: 0.13970932364463806, Val Loss: 0.3722061216831207
Epoch 2100/5000, Loss: 0.13890179991722107, Val Loss: 0.37179338932037354
Epoch 2105/5000, Loss: 0.13810424506664276, Val Loss: 0.37138307094573975
Epoch 2110/5000, Loss: 0.13731595873832703, Val Loss: 0.3709813952445984
Epoch 2115/5000, Loss: 0.13653771579265594, Val Loss: 0.37058332562446594
Epoch 2120/5000, Loss: 0.13576745986938477, Val Loss: 0.3701924979686737
Epoch 2125/5000, Loss: 0.1350060999393463, Val Loss: 0.3698088526725769
Epoch 2130/5000, Loss: 0.1342536211013794, Val Loss: 0.369424045085907
Epoch 2135/5000, Loss: 0.13350936770439148, Val Loss: 0.3690476417541504
Epoch 2140/5000, Loss: 0.13277313113212585, Val Loss: 0.368677020072937
Epoch 2145/5000, Loss: 0.13204450905323029, Val Loss: 0.36830881237983704
Epoch 2150/5000, Loss: 0.13132348656654358, Val Loss: 0.3679446578025818
Epoch 2155/5000, Loss: 0.13061019778251648, Val Loss: 0.36758896708488464
Epoch 2160/5000, Loss: 0.1299043446779251, Val Loss: 0.3672332763671875
Epoch 2165/5000, Loss: 0.1292056441307068, Val Loss: 0.36688607931137085
Epoch 2170/5000, Loss: 0.12851358950138092, Val Loss: 0.36653998494148254
Epoch 2175/5000, Loss: 0.12782879173755646, Val Loss: 0.366200715303421
Epoch 2180/5000, Loss: 0.12715084850788116, Val Loss: 0.3658639192581177
Epoch 2185/5000, Loss: 0.12647993862628937, Val Loss: 0.3655293583869934
Epoch 2190/5000, Loss: 0.1258152574300766, Val Loss: 0.36520084738731384
Epoch 2195/5000, Loss: 0.1251567006111145, Val Loss: 0.3648805618286133
Epoch 2200/5000, Loss: 0.12450555711984634, Val Loss: 0.3645576238632202
Epoch 2205/5000, Loss: 0.12386001646518707, Val Loss: 0.36423972249031067
Epoch 2210/5000, Loss: 0.1232207715511322, Val Loss: 0.3639256954193115
Epoch 2215/5000, Loss: 0.12258777022361755, Val Loss: 0.36361831426620483
Epoch 2220/5000, Loss: 0.12196076661348343, Val Loss: 0.36331087350845337
Epoch 2225/5000, Loss: 0.12133978307247162, Val Loss: 0.36300796270370483
Epoch 2230/5000, Loss: 0.12072449922561646, Val Loss: 0.36270689964294434
Epoch 2235/5000, Loss: 0.12011486291885376, Val Loss: 0.3624120354652405
Epoch 2240/5000, Loss: 0.11951104551553726, Val Loss: 0.3621177077293396
Epoch 2245/5000, Loss: 0.11891289800405502, Val Loss: 0.3618248701095581
Epoch 2250/5000, Loss: 0.11832022666931152, Val Loss: 0.3615388870239258
Epoch 2255/5000, Loss: 0.11773259937763214, Val Loss: 0.36125293374061584
Epoch 2260/5000, Loss: 0.11715062707662582, Val Loss: 0.36097267270088196
Epoch 2265/5000, Loss: 0.11657378077507019, Val Loss: 0.3606928288936615
Epoch 2270/5000, Loss: 0.11600235104560852, Val Loss: 0.3604167699813843
Epoch 2275/5000, Loss: 0.11543594300746918, Val Loss: 0.36014309525489807
Epoch 2280/5000, Loss: 0.11487479507923126, Val Loss: 0.3598712682723999
Epoch 2285/5000, Loss: 0.11431807279586792, Val Loss: 0.3596046566963196
Epoch 2290/5000, Loss: 0.11376655846834183, Val Loss: 0.3593398332595825
Epoch 2295/5000, Loss: 0.11322000622749329, Val Loss: 0.3590772747993469
Epoch 2300/5000, Loss: 0.11267764866352081, Val Loss: 0.3588164448738098

Epoch 2305/5000, Loss: 0.11214081943035126, Val Loss: 0.35855919122695923
Epoch 2310/5000, Loss: 0.11160844564437866, Val Loss: 0.3583054840564728
Epoch 2315/5000, Loss: 0.11108078062534332, Val Loss: 0.35805395245552063
Epoch 2320/5000, Loss: 0.11055788397789001, Val Loss: 0.35780635476112366
Epoch 2325/5000, Loss: 0.11003923416137695, Val Loss: 0.3575606644153595
Epoch 2330/5000, Loss: 0.10952482372522354, Val Loss: 0.3573208153247833
Epoch 2335/5000, Loss: 0.109015554189682, Val Loss: 0.3570809066295624
Epoch 2340/5000, Loss: 0.10851031541824341, Val Loss: 0.35684603452682495
Epoch 2345/5000, Loss: 0.10800924897193909, Val Loss: 0.3566082715988159
Epoch 2350/5000, Loss: 0.10751261562108994, Val Loss: 0.35637935996055603
Epoch 2355/5000, Loss: 0.10701995342969894, Val Loss: 0.3561464548110962
Epoch 2360/5000, Loss: 0.10653181374073029, Val Loss: 0.3559202551841736
Epoch 2365/5000, Loss: 0.10604678094387054, Val Loss: 0.3556971549987793
Epoch 2370/5000, Loss: 0.10556679964065552, Val Loss: 0.3554745614528656
Epoch 2375/5000, Loss: 0.1050904393196106, Val Loss: 0.35525771975517273
Epoch 2380/5000, Loss: 0.10461754351854324, Val Loss: 0.3550434708595276
Epoch 2385/5000, Loss: 0.10414890199899673, Val Loss: 0.354827880859375
Epoch 2390/5000, Loss: 0.10368429124355316, Val Loss: 0.3546141982078552
Epoch 2395/5000, Loss: 0.10322300344705582, Val Loss: 0.3544039726257324
Epoch 2400/5000, Loss: 0.10276539623737335, Val Loss: 0.3541966378688812
Epoch 2405/5000, Loss: 0.10231207311153412, Val Loss: 0.35398954153060913
Epoch 2410/5000, Loss: 0.10186222940683365, Val Loss: 0.3537874221801758
Epoch 2415/5000, Loss: 0.10141616314649582, Val Loss: 0.3535855710506439
Epoch 2420/5000, Loss: 0.10097329318523407, Val Loss: 0.35338619351387024
Epoch 2425/5000, Loss: 0.10053473711013794, Val Loss: 0.3531891107559204
Epoch 2430/5000, Loss: 0.10009899735450745, Val Loss: 0.35298940539360046
Epoch 2435/5000, Loss: 0.09966669231653214, Val Loss: 0.352794885635376
Epoch 2440/5000, Loss: 0.09923785924911499, Val Loss: 0.35260194540023804
Epoch 2445/5000, Loss: 0.09881237149238586, Val Loss: 0.35241052508354187
Epoch 2450/5000, Loss: 0.09839031100273132, Val Loss: 0.35222288966178894
Epoch 2455/5000, Loss: 0.09797144681215286, Val Loss: 0.3520351052284241
Epoch 2460/5000, Loss: 0.09755642712116241, Val Loss: 0.35184890031814575
Epoch 2465/5000, Loss: 0.09714390337467194, Val Loss: 0.35166454315185547
Epoch 2470/5000, Loss: 0.09673494845628738, Val Loss: 0.35148167610168457
Epoch 2475/5000, Loss: 0.09632924199104309, Val Loss: 0.3512987494468689
Epoch 2480/5000, Loss: 0.09592670202255249, Val Loss: 0.3511216640472412
Epoch 2485/5000, Loss: 0.0955272912979126, Val Loss: 0.35094377398490906
Epoch 2490/5000, Loss: 0.0951307862997055, Val Loss: 0.3507730960845947
Epoch 2495/5000, Loss: 0.09473736584186554, Val Loss: 0.350599080324173
Epoch 2500/5000, Loss: 0.09434668719768524, Val Loss: 0.35043108463287354
Epoch 2505/5000, Loss: 0.09395913034677505, Val Loss: 0.35026273131370544
Epoch 2510/5000, Loss: 0.09357466548681259, Val Loss: 0.3500957489013672
Epoch 2515/5000, Loss: 0.09319301694631577, Val Loss: 0.34992775321006775
Epoch 2520/5000, Loss: 0.09281434118747711, Val Loss: 0.349763959646225
Epoch 2525/5000, Loss: 0.09243857115507126, Val Loss: 0.3496016263961792
Epoch 2530/5000, Loss: 0.0920657142996788, Val Loss: 0.3494417369365692
Epoch 2535/5000, Loss: 0.0916956290602684, Val Loss: 0.34928205609321594
Epoch 2540/5000, Loss: 0.09132825583219528, Val Loss: 0.3491235077381134
Epoch 2545/5000, Loss: 0.09096361696720123, Val Loss: 0.34896811842918396
Epoch 2550/5000, Loss: 0.09060157835483551, Val Loss: 0.34881341457366943
Epoch 2555/5000, Loss: 0.09024232625961304, Val Loss: 0.34865802526474
Epoch 2560/5000, Loss: 0.08988569676876068, Val Loss: 0.3485051989555359
Epoch 2565/5000, Loss: 0.08953219652175903, Val Loss: 0.3483542203903198
Epoch 2570/5000, Loss: 0.08918069303035736, Val Loss: 0.34820646047592163
Epoch 2575/5000, Loss: 0.08883202821016312, Val Loss: 0.3480615019798279
Epoch 2580/5000, Loss: 0.08848605304956436, Val Loss: 0.3479127287864685
Epoch 2585/5000, Loss: 0.08814246207475662, Val Loss: 0.34777072072029114
Epoch 2590/5000, Loss: 0.08780141174793243, Val Loss: 0.3476254940032959
Epoch 2595/5000, Loss: 0.08746306598186493, Val Loss: 0.34748324751853943
Epoch 2600/5000, Loss: 0.08712679147720337, Val Loss: 0.3473472595214844
Epoch 2605/5000, Loss: 0.08679330348968506, Val Loss: 0.34720665216445923
Epoch 2610/5000, Loss: 0.0864621251821518, Val Loss: 0.34706783294677734
Epoch 2615/5000, Loss: 0.08613316714763641, Val Loss: 0.34693416953086853
Epoch 2620/5000, Loss: 0.08580663800239563, Val Loss: 0.34679850935935974
Epoch 2625/5000, Loss: 0.0854824036359787, Val Loss: 0.34666553139686584
Epoch 2630/5000, Loss: 0.08516091853380203, Val Loss: 0.34653154015541077
Epoch 2635/5000, Loss: 0.08484121412038803, Val Loss: 0.3464006781578064
Epoch 2640/5000, Loss: 0.08452406525611877, Val Loss: 0.3462699055671692
Epoch 2645/5000, Loss: 0.08420903235673904, Val Loss: 0.3461414575576782
Epoch 2650/5000, Loss: 0.0838964432477951, Val Loss: 0.34601470828056335
Epoch 2655/5000, Loss: 0.08358535170555115, Val Loss: 0.3458867073059082
Epoch 2660/5000, Loss: 0.08327730000019073, Val Loss: 0.3457581400871277
Epoch 2665/5000, Loss: 0.08297086507081985, Val Loss: 0.3456340432167053
Epoch 2670/5000, Loss: 0.082666777074337, Val Loss: 0.3455115556716919
Epoch 2675/5000, Loss: 0.08236472308635712, Val Loss: 0.34538891911506653
Epoch 2680/5000, Loss: 0.08206458389759064, Val Loss: 0.34526923298835754
Epoch 2685/5000, Loss: 0.08176666498184204, Val Loss: 0.3451480269432068
Epoch 2690/5000, Loss: 0.0814710259437561, Val Loss: 0.34502851963043213
Epoch 2695/5000, Loss: 0.0811769962310791, Val Loss: 0.34491166472435
Epoch 2700/5000, Loss: 0.08088511228561401, Val Loss: 0.34479740262031555
Epoch 2705/5000, Loss: 0.08059519529342651, Val Loss: 0.34468531608581543
Epoch 2710/5000, Loss: 0.08030734211206436, Val Loss: 0.34456875920295715
Epoch 2715/5000, Loss: 0.08002174645662308, Val Loss: 0.34445610642433167
Epoch 2720/5000, Loss: 0.07973776012659073, Val Loss: 0.3443446755409241
Epoch 2725/5000, Loss: 0.07945559173822403, Val Loss: 0.3442328870296478
Epoch 2730/5000, Loss: 0.07917564362287521, Val Loss: 0.3441248834133148
Epoch 2735/5000, Loss: 0.07889700680971146, Val Loss: 0.34401682019233704
Epoch 2740/5000, Loss: 0.0786208063364029, Val Loss: 0.34390804171562195
Epoch 2745/5000, Loss: 0.07834607362747192, Val Loss: 0.3438040614128113
Epoch 2750/5000, Loss: 0.07807312160730362, Val Loss: 0.34370049834251404
Epoch 2755/5000, Loss: 0.07780235260725021, Val Loss: 0.3435952663421631
Epoch 2760/5000, Loss: 0.07753299921751022, Val Loss: 0.34349167346954346
Epoch 2765/5000, Loss: 0.07726622372865677, Val Loss: 0.3433886170387268
Epoch 2770/5000, Loss: 0.07700058072805405, Val Loss: 0.34328654408454895
Epoch 2775/5000, Loss: 0.07673684507608414, Val Loss: 0.34318819642066956
Epoch 2780/5000, Loss: 0.07647491991519928, Val Loss: 0.34309062361717224
Epoch 2785/5000, Loss: 0.07621441036462784, Val Loss: 0.34299352765083313
Epoch 2790/5000, Loss: 0.07595603913068771, Val Loss: 0.34289756417274475
Epoch 2795/5000, Loss: 0.07569918781518936, Val Loss: 0.34280288219451904
Epoch 2800/5000, Loss: 0.07544386386871338, Val Loss: 0.3427080810070038
Epoch 2805/5000, Loss: 0.07519009709358215, Val Loss: 0.3426157832145691
Epoch 2810/5000, Loss: 0.07493824511766434, Val Loss: 0.34252187609672546
Epoch 2815/5000, Loss: 0.07468826323747635, Val Loss: 0.3424294590950012
Epoch 2820/5000, Loss: 0.07443972676992416, Val Loss: 0.3423357903957367
Epoch 2825/5000, Loss: 0.07419276237487793, Val Loss: 0.34224647283554077
Epoch 2830/5000, Loss: 0.07394713163375854, Val Loss: 0.3421580195426941
Epoch 2835/5000, Loss: 0.0737030878663063, Val Loss: 0.34207063913345337
Epoch 2840/5000, Loss: 0.0734608918428421, Val Loss: 0.3419847786426544
Epoch 2845/5000, Loss: 0.07322007417678833, Val Loss: 0.34189608693122864
Epoch 2850/5000, Loss: 0.07298082858324051, Val Loss: 0.3418098986148834
Epoch 2855/5000, Loss: 0.07274330407381058, Val Loss: 0.341724157333374
Epoch 2860/5000, Loss: 0.07250713557004929, Val Loss: 0.34164130687713623

Epoch 2865/5000, Loss: 0.07227247208356857, Val Loss: 0.34155765175819397
Epoch 2870/5000, Loss: 0.07203881442546844, Val Loss: 0.3414742052555084
Epoch 2875/5000, Loss: 0.07180722057819366, Val Loss: 0.3413963317871094
Epoch 2880/5000, Loss: 0.07157681882381439, Val Loss: 0.34131181240081787
Epoch 2885/5000, Loss: 0.071347676217556, Val Loss: 0.3412263095378876
Epoch 2890/5000, Loss: 0.07112032920122147, Val Loss: 0.3411473333835602
Epoch 2895/5000, Loss: 0.07089395821094513, Val Loss: 0.34107092022895813
Epoch 2900/5000, Loss: 0.07066955417394638, Val Loss: 0.34099066257476807
Epoch 2905/5000, Loss: 0.07044641673564911, Val Loss: 0.3409138023853302
Epoch 2910/5000, Loss: 0.07022421061992645, Val Loss: 0.34083735942840576
Epoch 2915/5000, Loss: 0.07000391185283661, Val Loss: 0.3407617509365082
Epoch 2920/5000, Loss: 0.06978435069322586, Val Loss: 0.34068387746810913
Epoch 2925/5000, Loss: 0.06956668943166733, Val Loss: 0.3406123220920563
Epoch 2930/5000, Loss: 0.0693502426147461, Val Loss: 0.3405389189720154
Epoch 2935/5000, Loss: 0.06913450360298157, Val Loss: 0.3404669463634491
Epoch 2940/5000, Loss: 0.06892078369855881, Val Loss: 0.3403961658477783
Epoch 2945/5000, Loss: 0.06870795786380768, Val Loss: 0.3403218388557434
Epoch 2950/5000, Loss: 0.06849662214517593, Val Loss: 0.3402552008628845
Epoch 2955/5000, Loss: 0.06828658282756805, Val Loss: 0.3401837646961212
Epoch 2960/5000, Loss: 0.06807789951562881, Val Loss: 0.3401148319244385
Epoch 2965/5000, Loss: 0.06787004321813583, Val Loss: 0.3400442898273468
Epoch 2970/5000, Loss: 0.06766429543495178, Val Loss: 0.3399770259857178
Epoch 2975/5000, Loss: 0.06745898723602295, Val Loss: 0.3399096131324768
Epoch 2980/5000, Loss: 0.06725466996431351, Val Loss: 0.3398396372795105
Epoch 2985/5000, Loss: 0.06705188751220703, Val Loss: 0.3397720456123352
Epoch 2990/5000, Loss: 0.06685063987970352, Val Loss: 0.33970746397972107
Epoch 2995/5000, Loss: 0.0666503757238388, Val Loss: 0.3396441340446472
Epoch 3000/5000, Loss: 0.06645119190216064, Val Loss: 0.3395826816558838
Epoch 3005/5000, Loss: 0.066253162920475, Val Loss: 0.3395135998725891
Epoch 3010/5000, Loss: 0.06605635583400726, Val Loss: 0.33945411443710327
Epoch 3015/5000, Loss: 0.06586046516895294, Val Loss: 0.3393891453742981
Epoch 3020/5000, Loss: 0.06566620618104935, Val Loss: 0.3393283486366272
Epoch 3025/5000, Loss: 0.06547286361455917, Val Loss: 0.33926475048065186
Epoch 3030/5000, Loss: 0.06528041511774063, Val Loss: 0.33920130133628845
Epoch 3035/5000, Loss: 0.06508932262659073, Val Loss: 0.33914127945899963
Epoch 3040/5000, Loss: 0.06489921361207962, Val Loss: 0.3390820622444153
Epoch 3045/5000, Loss: 0.06471031904220581, Val Loss: 0.3390201926231384
Epoch 3050/5000, Loss: 0.06452269852161407, Val Loss: 0.33896318078041077
Epoch 3055/5000, Loss: 0.06433573365211487, Val Loss: 0.33890393376350403
Epoch 3060/5000, Loss: 0.0641496554017067, Val Loss: 0.3388477563858032
Epoch 3065/5000, Loss: 0.06396469473838806, Val Loss: 0.33878612518310547
Epoch 3070/5000, Loss: 0.06378106772899628, Val Loss: 0.3387318253517151
Epoch 3075/5000, Loss: 0.0635981485247612, Val Loss: 0.33867791295051575
Epoch 3080/5000, Loss: 0.06341614574193954, Val Loss: 0.338621586561203
Epoch 3085/5000, Loss: 0.06323565542697906, Val Loss: 0.3385685980319977
Epoch 3090/5000, Loss: 0.06305589526891708, Val Loss: 0.3385121822357178
Epoch 3095/5000, Loss: 0.06287713348865509, Val Loss: 0.33845457434654236
Epoch 3100/5000, Loss: 0.06269943714141846, Val Loss: 0.3384040892124176
Epoch 3105/5000, Loss: 0.06252261996269226, Val Loss: 0.3383505940437317
Epoch 3110/5000, Loss: 0.0623469315469265, Val Loss: 0.33829671144485474
Epoch 3115/5000, Loss: 0.06217234581708908, Val Loss: 0.3382438123226166
Epoch 3120/5000, Loss: 0.061998799443244934, Val Loss: 0.33819475769996643
Epoch 3125/5000, Loss: 0.061826255172491074, Val Loss: 0.33813899755477905
Epoch 3130/5000, Loss: 0.06165419891476631, Val Loss: 0.3380890488624573
Epoch 3135/5000, Loss: 0.061483316123485565, Val Loss: 0.3380383849143982
Epoch 3140/5000, Loss: 0.06131305545568466, Val Loss: 0.3379838168621063
Epoch 3145/5000, Loss: 0.06114356592297554, Val Loss: 0.3379344344139099
Epoch 3150/5000, Loss: 0.06097565218806267, Val Loss: 0.337881863117218
Epoch 3155/5000, Loss: 0.060807958245277405, Val Loss: 0.3378341495990753
Epoch 3160/5000, Loss: 0.060641322284936905, Val Loss: 0.33778607845306396
Epoch 3165/5000, Loss: 0.060476064682006836, Val Loss: 0.33773544430732727
Epoch 3170/5000, Loss: 0.060311440378427505, Val Loss: 0.3376859426498413
Epoch 3175/5000, Loss: 0.06014743447303772, Val Loss: 0.33763784170150757
Epoch 3180/5000, Loss: 0.059984881430864334, Val Loss: 0.33758893609046936
Epoch 3185/5000, Loss: 0.05982262268662453, Val Loss: 0.33753713965415955
Epoch 3190/5000, Loss: 0.05966157838702202, Val Loss: 0.33749517798423767
Epoch 3195/5000, Loss: 0.05950139835476875, Val Loss: 0.33745086193084717
Epoch 3200/5000, Loss: 0.05934175103902817, Val Loss: 0.33740195631980896
Epoch 3205/5000, Loss: 0.05918341875076294, Val Loss: 0.33735135197639465
Epoch 3210/5000, Loss: 0.05902569368481636, Val Loss: 0.3373106122016907
Epoch 3215/5000, Loss: 0.05886929854750633, Val Loss: 0.33726292848587036
Epoch 3220/5000, Loss: 0.0587129108607769, Val Loss: 0.3372170925140381
Epoch 3225/5000, Loss: 0.058557841926813126, Val Loss: 0.3371717035770416
Epoch 3230/5000, Loss: 0.05840369686484337, Val Loss: 0.33713117241859436
Epoch 3235/5000, Loss: 0.058249711990356445, Val Loss: 0.3370853662490845
Epoch 3240/5000, Loss: 0.05809736251831055, Val Loss: 0.33703935146331787
Epoch 3245/5000, Loss: 0.057945746928453445, Val Loss: 0.3369944989681244
Epoch 3250/5000, Loss: 0.05779465660452843, Val Loss: 0.33695247769355774
Epoch 3255/5000, Loss: 0.05764447897672653, Val Loss: 0.33690953254699707
Epoch 3260/5000, Loss: 0.05749516189098358, Val Loss: 0.3368682265281677
Epoch 3265/5000, Loss: 0.05734652653336525, Val Loss: 0.3368253707885742
Epoch 3270/5000, Loss: 0.05719858407974243, Val Loss: 0.336780846118927
Epoch 3275/5000, Loss: 0.05705135315656662, Val Loss: 0.3367394804954529
Epoch 3280/5000, Loss: 0.05690527707338333, Val Loss: 0.3367007076740265
Epoch 3285/5000, Loss: 0.056759923696517944, Val Loss: 0.3366607427597046
Epoch 3290/5000, Loss: 0.05661514028906822, Val Loss: 0.3366228938102722
Epoch 3295/5000, Loss: 0.056470971554517746, Val Loss: 0.3365812599658966
Epoch 3300/5000, Loss: 0.05632795765995979, Val Loss: 0.33654266595840454
Epoch 3305/5000, Loss: 0.05618564784526825, Val Loss: 0.3365009129047394
Epoch 3310/5000, Loss: 0.05604379251599312, Val Loss: 0.33646535873413086
Epoch 3315/5000, Loss: 0.055902957916259766, Val Loss: 0.33642664551734924
Epoch 3320/5000, Loss: 0.05576273798942566, Val Loss: 0.3363897502422333
Epoch 3325/5000, Loss: 0.055623337626457214, Val Loss: 0.3363538980484009
Epoch 3330/5000, Loss: 0.055484477430582047, Val Loss: 0.33631691336631775
Epoch 3335/5000, Loss: 0.05534668266773224, Val Loss: 0.3362767696380615
Epoch 3340/5000, Loss: 0.05520932376384735, Val Loss: 0.33624497056007385
Epoch 3345/5000, Loss: 0.05507264658808708, Val Loss: 0.3362067937850952
Epoch 3350/5000, Loss: 0.05493665859103203, Val Loss: 0.3361702859401703
Epoch 3355/5000, Loss: 0.05480141192674637, Val Loss: 0.33613505959510803
Epoch 3360/5000, Loss: 0.05466683954000473, Val Loss: 0.3361011743545532
Epoch 3365/5000, Loss: 0.05453303083777428, Val Loss: 0.33606842160224915
Epoch 3370/5000, Loss: 0.0544000118970871, Val Loss: 0.336031049489975
Epoch 3375/5000, Loss: 0.05426742881536484, Val Loss: 0.3360004425048828
Epoch 3380/5000, Loss: 0.054135605692863464, Val Loss: 0.33596426248550415
Epoch 3385/5000, Loss: 0.054004427045583725, Val Loss: 0.33593252301216125
Epoch 3390/5000, Loss: 0.053874123841524124, Val Loss: 0.33589908480644226
Epoch 3395/5000, Loss: 0.05374433472752571, Val Loss: 0.3358669579029083
Epoch 3400/5000, Loss: 0.053614914417266846, Val Loss: 0.3358357846736908
Epoch 3405/5000, Loss: 0.05348651856184006, Val Loss: 0.33580145239830017
Epoch 3410/5000, Loss: 0.053358618170022964, Val Loss: 0.3357704281806946
Epoch 3415/5000, Loss: 0.053231533616781235, Val Loss: 0.335741251707077
Epoch 3420/5000, Loss: 0.053104691207408905, Val Loss: 0.33571162819862366

Epoch 3425/5000, Loss: 0.052978888154029846, Val Loss: 0.3356807231903076
Epoch 3430/5000, Loss: 0.05285358428955078, Val Loss: 0.33565232157707214
Epoch 3435/5000, Loss: 0.05272865667939186, Val Loss: 0.3356280028820038
Epoch 3440/5000, Loss: 0.05260464921593666, Val Loss: 0.33559489250183105
Epoch 3445/5000, Loss: 0.052481215447187424, Val Loss: 0.33556830883026123
Epoch 3450/5000, Loss: 0.05235828831791878, Val Loss: 0.3355422616004944
Epoch 3455/5000, Loss: 0.05223630741238594, Val Loss: 0.33551403880119324
Epoch 3460/5000, Loss: 0.05211431160569191, Val Loss: 0.3354857265949249
Epoch 3465/5000, Loss: 0.05199355632066727, Val Loss: 0.33546018600463867
Epoch 3470/5000, Loss: 0.0518730953335762, Val Loss: 0.33542877435684204
Epoch 3475/5000, Loss: 0.051753416657447815, Val Loss: 0.33540287613868713
Epoch 3480/5000, Loss: 0.05163412168622017, Val Loss: 0.33537498116493225
Epoch 3485/5000, Loss: 0.05151544511318207, Val Loss: 0.33534863591194153
Epoch 3490/5000, Loss: 0.05139729753136635, Val Loss: 0.3353237211704254
Epoch 3495/5000, Loss: 0.051280129700899124, Val Loss: 0.3352958858013153
Epoch 3500/5000, Loss: 0.051163312047719955, Val Loss: 0.3352705240249634
Epoch 3505/5000, Loss: 0.05104702711105347, Val Loss: 0.3352453410625458
Epoch 3510/5000, Loss: 0.05093134567141533, Val Loss: 0.3352203965187073
Epoch 3515/5000, Loss: 0.050816357135772705, Val Loss: 0.33520156145095825
Epoch 3520/5000, Loss: 0.050701871514320374, Val Loss: 0.3351741433143616
Epoch 3525/5000, Loss: 0.05058787018060684, Val Loss: 0.3351498246192932
Epoch 3530/5000, Loss: 0.05047415569424629, Val Loss: 0.3351249694824219
Epoch 3535/5000, Loss: 0.05036135017871857, Val Loss: 0.33510375022888184
Epoch 3540/5000, Loss: 0.05024896189570427, Val Loss: 0.33507785201072693
Epoch 3545/5000, Loss: 0.05013678967952728, Val Loss: 0.3350571095943451
Epoch 3550/5000, Loss: 0.05002545565366745, Val Loss: 0.3350363075733185
Epoch 3555/5000, Loss: 0.04991514980792999, Val Loss: 0.33501365780830383
Epoch 3560/5000, Loss: 0.0498046837747097, Val Loss: 0.3349921703338623
Epoch 3565/5000, Loss: 0.04969528689980507, Val Loss: 0.33497023582458496
Epoch 3570/5000, Loss: 0.04958609491586685, Val Loss: 0.3349480628967285
Epoch 3575/5000, Loss: 0.04947732388973236, Val Loss: 0.33492588996887207
Epoch 3580/5000, Loss: 0.049369264394044876, Val Loss: 0.3349071145057678
Epoch 3585/5000, Loss: 0.049261368811130524, Val Loss: 0.3348851799964905
Epoch 3590/5000, Loss: 0.049154557287693024, Val Loss: 0.3348631262779236
Epoch 3595/5000, Loss: 0.04904796555638313, Val Loss: 0.33484238386154175
Epoch 3600/5000, Loss: 0.048941999673843384, Val Loss: 0.33482345938682556
Epoch 3605/5000, Loss: 0.04883638769388199, Val Loss: 0.33480310440063477
Epoch 3610/5000, Loss: 0.048731476068496704, Val Loss: 0.3347841799259186
Epoch 3615/5000, Loss: 0.048627037554979324, Val Loss: 0.3347679376602173
Epoch 3620/5000, Loss: 0.048522789031267166, Val Loss: 0.33475178480148315
Epoch 3625/5000, Loss: 0.04841931536793709, Val Loss: 0.33473679423332214
Epoch 3630/5000, Loss: 0.048316143453121185, Val Loss: 0.33471769094467163
Epoch 3635/5000, Loss: 0.048213496804237366, Val Loss: 0.33470383286476135
Epoch 3640/5000, Loss: 0.04811134189367294, Val Loss: 0.3346864581108093
Epoch 3645/5000, Loss: 0.048009663820266724, Val Loss: 0.3346688449382782
Epoch 3650/5000, Loss: 0.04790849611163139, Val Loss: 0.33465373516082764
Epoch 3655/5000, Loss: 0.047807615250349045, Val Loss: 0.3346339166164398
Epoch 3660/5000, Loss: 0.047707583755254745, Val Loss: 0.33461856842041016
Epoch 3665/5000, Loss: 0.04760747402906418, Val Loss: 0.3346036374568939
Epoch 3670/5000, Loss: 0.04750867933034897, Val Loss: 0.3345857262611389
Epoch 3675/5000, Loss: 0.047409866005182266, Val Loss: 0.3345705270767212
Epoch 3680/5000, Loss: 0.04731117561459541, Val Loss: 0.33455803990364075
Epoch 3685/5000, Loss: 0.04721299931406975, Val Loss: 0.3345422148704529
Epoch 3690/5000, Loss: 0.047115832567214966, Val Loss: 0.3345284163951874
Epoch 3695/5000, Loss: 0.04701881855726242, Val Loss: 0.33451226353645325
Epoch 3700/5000, Loss: 0.046921879053115845, Val Loss: 0.33449846506118774
Epoch 3705/5000, Loss: 0.0468258298933506, Val Loss: 0.3344825208187103
Epoch 3710/5000, Loss: 0.046730201691389084, Val Loss: 0.33447229862213135
Epoch 3715/5000, Loss: 0.046635210514068604, Val Loss: 0.33445560932159424
Epoch 3720/5000, Loss: 0.04654007405042648, Val Loss: 0.33443954586982727
Epoch 3725/5000, Loss: 0.046446047723293304, Val Loss: 0.3344265818595886
Epoch 3730/5000, Loss: 0.04635190963745117, Val Loss: 0.33441126346588135
Epoch 3735/5000, Loss: 0.046258166432380676, Val Loss: 0.3343983590602875
Epoch 3740/5000, Loss: 0.046165235340595245, Val Loss: 0.33438900113105774
Epoch 3745/5000, Loss: 0.046072687953710556, Val Loss: 0.3343745768070221
Epoch 3750/5000, Loss: 0.04598040133714676, Val Loss: 0.33436259627342224
Epoch 3755/5000, Loss: 0.045888885855674744, Val Loss: 0.33434760570526123
Epoch 3760/5000, Loss: 0.045797187834978104, Val Loss: 0.33433616161346436
Epoch 3765/5000, Loss: 0.04570625349879265, Val Loss: 0.3343249559402466
Epoch 3770/5000, Loss: 0.04561584070324898, Val Loss: 0.33431291580200195
Epoch 3775/5000, Loss: 0.04552586376667023, Val Loss: 0.3343032896518707
Epoch 3780/5000, Loss: 0.04543600603938103, Val Loss: 0.3342897295951843
Epoch 3785/5000, Loss: 0.04534682258963585, Val Loss: 0.334273099899292
Epoch 3790/5000, Loss: 0.0452580526471138, Val Loss: 0.33426550030708313
Epoch 3795/5000, Loss: 0.04516967758536339, Val Loss: 0.33425483107566833
Epoch 3800/5000, Loss: 0.0450814813375473, Val Loss: 0.3342452943325043
Epoch 3805/5000, Loss: 0.04499391093850136, Val Loss: 0.33423200249671936
Epoch 3810/5000, Loss: 0.044906679540872574, Val Loss: 0.3342196047306061
Epoch 3815/5000, Loss: 0.04481983184814453, Val Loss: 0.3342115879058838
Epoch 3820/5000, Loss: 0.04473329335451126, Val Loss: 0.3341982364654541
Epoch 3825/5000, Loss: 0.044646967202425, Val Loss: 0.33418989181518555
Epoch 3830/5000, Loss: 0.044561222195625305, Val Loss: 0.3341763913631439
Epoch 3835/5000, Loss: 0.04447576403617859, Val Loss: 0.3341672122478485
Epoch 3840/5000, Loss: 0.04439074546098709, Val Loss: 0.33415767550468445
Epoch 3845/5000, Loss: 0.04430602863430977, Val Loss: 0.33414414525032043
Epoch 3850/5000, Loss: 0.04422196373343468, Val Loss: 0.33413785696029663
Epoch 3855/5000, Loss: 0.04413805902004242, Val Loss: 0.33412885665893555
Epoch 3860/5000, Loss: 0.04405444860458374, Val Loss: 0.3341203033924103
Epoch 3865/5000, Loss: 0.043971411883831024, Val Loss: 0.3341104984283447
Epoch 3870/5000, Loss: 0.04388852417469025, Val Loss: 0.3341006934642792
Epoch 3875/5000, Loss: 0.0438060387969017, Val Loss: 0.33409368991851807
Epoch 3880/5000, Loss: 0.043724339455366135, Val Loss: 0.3340805172920227
Epoch 3885/5000, Loss: 0.04364228993654251, Val Loss: 0.3340720534324646
Epoch 3890/5000, Loss: 0.043560996651649475, Val Loss: 0.33406496047973633
Epoch 3895/5000, Loss: 0.04347989335656166, Val Loss: 0.3340563178062439
Epoch 3900/5000, Loss: 0.043399300426244736, Val Loss: 0.33404767513275146
Epoch 3905/5000, Loss: 0.0433189757168293, Val Loss: 0.3340436816215515
Epoch 3910/5000, Loss: 0.04323887079954147, Val Loss: 0.3340395390987396
Epoch 3915/5000, Loss: 0.04315928742289543, Val Loss: 0.3340289890766144
Epoch 3920/5000, Loss: 0.04308020696043968, Val Loss: 0.33402159810066223
Epoch 3925/5000, Loss: 0.04300129413604736, Val Loss: 0.3340146243572235
Epoch 3930/5000, Loss: 0.04292271286249161, Val Loss: 0.3340088427066803
Epoch 3935/5000, Loss: 0.04284413158893585, Val Loss: 0.33399519324302673
Epoch 3940/5000, Loss: 0.04276641458272934, Val Loss: 0.3339911103248596
Epoch 3945/5000, Loss: 0.04268888756632805, Val Loss: 0.3339858949184418
Epoch 3950/5000, Loss: 0.04261157661676407, Val Loss: 0.33397647738456726
Epoch 3955/5000, Loss: 0.04253479465842247, Val Loss: 0.33396878838539124
Epoch 3960/5000, Loss: 0.04245820268988609, Val Loss: 0.3339619040489197
Epoch 3965/5000, Loss: 0.04238184168934822, Val Loss: 0.33395662903785706
Epoch 3970/5000, Loss: 0.04230589419603348, Val Loss: 0.3339499235153198
Epoch 3975/5000, Loss: 0.04223024472594261, Val Loss: 0.3339444696903229
Epoch 3980/5000, Loss: 0.042154885828495026, Val Loss: 0.33393368124961853

Epoch 3985/5000, Loss: 0.042079824954271317, Val Loss: 0.3339267373085022
Epoch 3990/5000, Loss: 0.042005106806755066, Val Loss: 0.3339255750179291
Epoch 3995/5000, Loss: 0.04193059355020523, Val Loss: 0.3339165449142456
Epoch 4000/5000, Loss: 0.041856732219457626, Val Loss: 0.33391162753105164
Epoch 4005/5000, Loss: 0.04178284481167793, Val Loss: 0.33390823006629944
Epoch 4010/5000, Loss: 0.0417095348238945, Val Loss: 0.33390119671821594
Epoch 4015/5000, Loss: 0.04163608327507973, Val Loss: 0.3338981866836548
Epoch 4020/5000, Loss: 0.04156329482793808, Val Loss: 0.3338911533355713
Epoch 4025/5000, Loss: 0.04149070009589195, Val Loss: 0.33388984203338623
Epoch 4030/5000, Loss: 0.04141853004693985, Val Loss: 0.3338874280452728
Epoch 4035/5000, Loss: 0.041346367448568344, Val Loss: 0.33388057351112366
Epoch 4040/5000, Loss: 0.04127482697367668, Val Loss: 0.33387652039527893
Epoch 4045/5000, Loss: 0.0412033274769783, Val Loss: 0.33387264609336853
Epoch 4050/5000, Loss: 0.04113216698169708, Val Loss: 0.333869993686676
Epoch 4055/5000, Loss: 0.0410616509616375, Val Loss: 0.3338644802570343
Epoch 4060/5000, Loss: 0.04099126532673836, Val Loss: 0.3338627517223358
Epoch 4065/5000, Loss: 0.040920939296483994, Val Loss: 0.33385995030403137
Epoch 4070/5000, Loss: 0.04085107892751694, Val Loss: 0.3338606655597687
Epoch 4075/5000, Loss: 0.040781233459711075, Val Loss: 0.3338574469089508
Epoch 4080/5000, Loss: 0.04071223363280296, Val Loss: 0.3338501751422882
Epoch 4085/5000, Loss: 0.04064291715621948, Val Loss: 0.3338465988636017
Epoch 4090/5000, Loss: 0.04057438299059868, Val Loss: 0.33384817838668823
Epoch 4095/5000, Loss: 0.0405057854950428, Val Loss: 0.3338488042354584
Epoch 4100/5000, Loss: 0.04043731465935707, Val Loss: 0.3338397741317749
Epoch 4105/5000, Loss: 0.040369369089603424, Val Loss: 0.3338381052017212
Epoch 4110/5000, Loss: 0.040301766246557236, Val Loss: 0.33383527398109436
Epoch 4115/5000, Loss: 0.04023459553718567, Val Loss: 0.3338264226913452
Epoch 4120/5000, Loss: 0.04016752913594246, Val Loss: 0.33382853865623474
Epoch 4125/5000, Loss: 0.040100451558828354, Val Loss: 0.33382824063301086
Epoch 4130/5000, Loss: 0.0400337390601635, Val Loss: 0.33382436633110046
Epoch 4135/5000, Loss: 0.039967648684978485, Val Loss: 0.3338280916213989
Epoch 4140/5000, Loss: 0.039901286363601685, Val Loss: 0.33382222056388855
Epoch 4145/5000, Loss: 0.03983555734157562, Val Loss: 0.3338256776332855
Epoch 4150/5000, Loss: 0.03977002203464508, Val Loss: 0.3338211476802826
Epoch 4155/5000, Loss: 0.039704471826553345, Val Loss: 0.333819180727005
Epoch 4160/5000, Loss: 0.03963940963149071, Val Loss: 0.3338174819946289
Epoch 4165/5000, Loss: 0.03957473859190941, Val Loss: 0.3338189423084259
Epoch 4170/5000, Loss: 0.039510272443294525, Val Loss: 0.33381736278533936
Epoch 4175/5000, Loss: 0.03944593295454979, Val Loss: 0.33381399512290955
Epoch 4180/5000, Loss: 0.039381884038448334, Val Loss: 0.33380991220474243
Epoch 4185/5000, Loss: 0.03931771591305733, Val Loss: 0.3338148891925812
Epoch 4190/5000, Loss: 0.03925438970327377, Val Loss: 0.33381131291389465
Stopping early at epoch 4192.

#Accuracy on validation set
model.eval()
with torch.no_grad():
    val_outputs = model(X_val)
    val_predicted_bin = (val_outputs.squeeze() > 0.5).int()
    
    #validation accuracy
    val_accuracy = accuracy_score(y_val.numpy(), val_predicted_bin.numpy())
    print(f'Validation set accuracy: {val_accuracy:.2f}')
    
# Resources:
# https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html
# https://pytorch.org/docs/stable/generated/torch.Tensor.numpy.html
# https://www.youtube.com/watch?v=E35CVhVKISA&t=135s

Validation set accuracy: 0.8479623824451411

#Accuracy on test set
model.eval()

with torch.no_grad():
    test_outputs = model(X_test)
    predicted_bin = (test_outputs.squeeze() > 0.5).int()
    
#accuracy
test_accuracy = accuracy_score(y_test.numpy(), predicted_bin.numpy())
print(f'Accuracy on test set: {test_accuracy:.2f}')

#classification report
print(classification_report(y_test.numpy(), predicted_bin.numpy()))

Accuracy on test set: 0.84
              precision    recall  f1-score   support

         0.0       0.87      0.83      0.85       395
         1.0       0.82      0.86      0.84       355

    accuracy                           0.84       750
   macro avg       0.84      0.84      0.84       750
weighted avg       0.85      0.84      0.84       750

#Turning tensors into NumPy arrays so we can use Scikit-learn functions
test_outputs_np = test_outputs.squeeze().numpy()
y_test_np = y_test.numpy()

#Calculation of the ROC-AU
fpr, tpr, thresholds = roc_curve(y_test_np, test_outputs_np)
roc_auc = roc_auc_score(y_test_np, test_outputs_np)

#Plotting the ROC curve
plt.figure(figsize=(8, 6))

plt.plot(fpr, tpr, color='firebrick', lw=2, label='ROC curve (area = %0.2f)' % roc_auc)
plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.0])

plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('ROC-AU Curve: MLP Model')
plt.legend(loc="lower right")

plt.show()

#Calculation of the confusion matrix
cm = confusion_matrix(y_test.numpy(), predicted_bin.numpy())

#Plotting the confusion matrix
plt.figure(figsize=(8, 6))

sns.heatmap(cm, annot=True, fmt="d", cmap='seismic')
plt.xlabel('Predicted labels')
plt.ylabel('True labels')
plt.title('Confusion Matrix: MLP Model')

plt.show()

#Total Time Consumed 
end_time = time.time()
execution_time = end_time - start_time
print(f"Total Execution Time: {execution_time} seconds")

Total Execution Time: 11198.268087863922 seconds

Note about the code:¶

1) Importing data set¶

2) Reducing size¶

3) Preprocessing¶

3.1) Removing HTML tags and URLs, lower¶

3.2) Tokenization and stopwords elimination.¶

Statistic Analysis ¶

.....................Starts¶

A) Checking text¶

B) Avg Words Positive Vs Negative:¶

C) Word Frequency¶

D) Unique Words¶

E) Most common words¶

Statistic Analysis ¶

....................................Finishes¶

Sentiment Analysis Continues¶

4) Splitting Data¶

5) TF-IDF Calculation¶

6) Format conversion¶

7) MLP model¶

Note about the code:¶

1) Importing data set¶

2) Reducing size¶

3) Preprocessing¶

3.1) Removing HTML tags and URLs, lower¶

3.2) Tokenization and stopwords elimination.¶

** Statistic Analysis **¶

.....................Starts¶

A) Checking text¶

B) Avg Words Positive Vs Negative:¶

C) Word Frequency¶

D) Unique Words¶

E) Most common words¶

** Statistic Analysis **¶

....................................Finishes¶

Sentiment Analysis Continues¶

4) Splitting Data¶

5) TF-IDF Calculation¶

6) Format conversion¶

7) MLP model¶

Statistic Analysis ¶

Statistic Analysis ¶