dlnd_tv_script_generation


TV Script Generation

In this project, you'll generate your own Simpsons TV scripts using RNNs. You'll be using part of the Simpsons dataset of scripts from 27 seasons. The Neural Network you'll build will generate a new TV script for a scene at Moe's Tavern.

Get the Data

The data is already provided for you. You'll be using a subset of the original dataset. It consists of only the scenes in Moe's Tavern. This doesn't include other versions of the tavern, like "Moe's Cavern", "Flaming Moe's", "Uncle Moe's Family Feed-Bag", etc..


In [1]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

data_dir = './data/simpsons/moes_tavern_lines.txt'
text = helper.load_data(data_dir)
# Ignore notice, since we don't use it for analysing the data
text = text[81:]

Explore the Data

Play around with view_sentence_range to view different parts of the data.


In [2]:
view_sentence_range = (0, 10)

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

print('Dataset Stats')
print('Roughly the number of unique words: {}'.format(len({word: None for word in text.split()})))
scenes = text.split('\n\n')
print('Number of scenes: {}'.format(len(scenes)))
sentence_count_scene = [scene.count('\n') for scene in scenes]
print('Average number of sentences in each scene: {}'.format(np.average(sentence_count_scene)))

sentences = [sentence for scene in scenes for sentence in scene.split('\n')]
print('Number of lines: {}'.format(len(sentences)))
word_count_sentence = [len(sentence.split()) for sentence in sentences]
print('Average number of words in each line: {}'.format(np.average(word_count_sentence)))

print()
print('The sentences {} to {}:'.format(*view_sentence_range))
print('\n'.join(text.split('\n')[view_sentence_range[0]:view_sentence_range[1]]))


Dataset Stats
Roughly the number of unique words: 11492
Number of scenes: 262
Average number of sentences in each scene: 15.248091603053435
Number of lines: 4257
Average number of words in each line: 11.50434578341555

The sentences 0 to 10:
Moe_Szyslak: (INTO PHONE) Moe's Tavern. Where the elite meet to drink.
Bart_Simpson: Eh, yeah, hello, is Mike there? Last name, Rotch.
Moe_Szyslak: (INTO PHONE) Hold on, I'll check. (TO BARFLIES) Mike Rotch. Mike Rotch. Hey, has anybody seen Mike Rotch, lately?
Moe_Szyslak: (INTO PHONE) Listen you little puke. One of these days I'm gonna catch you, and I'm gonna carve my name on your back with an ice pick.
Moe_Szyslak: What's the matter Homer? You're not your normal effervescent self.
Homer_Simpson: I got my problems, Moe. Give me another one.
Moe_Szyslak: Homer, hey, you should not drink to forget your problems.
Barney_Gumble: Yeah, you should only drink to enhance your social skills.


Implement Preprocessing Functions

The first thing to do to any dataset is preprocessing. Implement the following preprocessing functions below:

  • Lookup Table
  • Tokenize Punctuation

Lookup Table

To create a word embedding, you first need to transform the words to ids. In this function, create two dictionaries:

  • Dictionary to go from the words to an id, we'll call vocab_to_int
  • Dictionary to go from the id to word, we'll call int_to_vocab

Return these dictionaries in the following tuple (vocab_to_int, int_to_vocab)


In [3]:
import numpy as np
import problem_unittests as tests
import re

def create_lookup_tables(text):
    """
    Create lookup tables for vocabulary
    :param text: The text of tv scripts split into words
    :return: A tuple of dicts (vocab_to_int, int_to_vocab)
    """

    #text = ''.join([c.lower()+' ' for c in text])
    #text_for_lookup  = sorted(list(set(re.split('(\W)+', text))))
    #marks_for_lookup = sorted(list(set(re.split('(\w)+', text))))
    #text_for_lookup  = sorted(list(set(text_for_lookup + (marks_for_lookup))))
    #test = re.split(' ', text)
    text_for_lookup = sorted(list(set(text)))

    int_to_vocab = dict((i, c) for i, c in enumerate(text_for_lookup))
    vocab_to_int = dict((c, i) for i, c in enumerate(text_for_lookup))

    return vocab_to_int, int_to_vocab

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_create_lookup_tables(create_lookup_tables)


Tests Passed

Tokenize Punctuation

We'll be splitting the script into a word array using spaces as delimiters. However, punctuations like periods and exclamation marks make it hard for the neural network to distinguish between the word "bye" and "bye!".

Implement the function token_lookup to return a dict that will be used to tokenize symbols like "!" into "Exclamation_Mark". Create a dictionary for the following symbols where the symbol is the key and value is the token:

  • Period ( . )
  • Comma ( , )
  • Quotation Mark ( " )
  • Semicolon ( ; )
  • Exclamation mark ( ! )
  • Question mark ( ? )
  • Left Parentheses ( ( )
  • Right Parentheses ( ) )
  • Dash ( -- )
  • Return ( \n )

This dictionary will be used to token the symbols and add the delimiter (space) around it. This separates the symbols as it's own word, making it easier for the neural network to predict on the next word. Make sure you don't use a token that could be confused as a word. Instead of using the token "dash", try using something like "dash".


In [5]:
def token_lookup():
    """
    Generate a dict to turn punctuation into a token.
    :return: Tokenize dictionary where the key is the punctuation and the value is the token
    """
    symbols = (['.', ',', '"', ';', '!', '?', '(', ')', '--', '\n'#, ':', '\'', '#', '/', '%'
               ])
    
    symbol_desc = ([
        '__period__',
        '__comma__',
        '__quotation_Mark__',
        '__semicolon__',
        '__exclamation_mark__',
        '__question_mark__',
        '__left_Parentheses__',
        '__right_Parentheses__',
        '__dash__',
        '__return__',
        #'__double_point__', ## missing in description
        #'__quote__',        ## missing in description
        #'__hash__',         ## missing in description
        #'__slash__',        ## missing in description
        #'__procent__',      ## missing in description
        ])
    
    return dict(zip(symbols,symbol_desc))

token_lookup()


Out[5]:
{'\n': '__return__',
 '!': '__exclamation_mark__',
 '"': '__quotation_Mark__',
 '(': '__left_Parentheses__',
 ')': '__right_Parentheses__',
 ',': '__comma__',
 '--': '__dash__',
 '.': '__period__',
 ';': '__semicolon__',
 '?': '__question_mark__'}

In [6]:
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_tokenize(token_lookup)


Tests Passed

Preprocess all the data and save it

Running the code cell below will preprocess all the data and save it to file.


In [7]:
# debug helper 
text = helper.load_data(data_dir)

# Ignore notice, since we don't use it for analysing the data
text = text[81:]

token_dict = token_lookup()
for key, token in token_dict.items():
    text = text.replace(key, ' {} '.format(token))

text = text.lower()
text = text.split()

vocab_to_int, int_to_vocab = create_lookup_tables(text)

print(vocab_to_int)


{'caricature': 951, 'billion': 620, "life's": 3415, 'she-pu': 5213, 'extra': 2034, 'dealt': 1512, 'form': 2284, 'fell': 2124, 'rebuilt': 4770, 'if': 2952, "dyin'": 1816, 'breathalyzer': 769, 'municipal': 3911, 'philosophic': 4364, 'mull': 3905, 'beep': 553, 'fence': 2134, "nothin's": 4073, 'grammar': 2534, "workin'": 6664, 'cutie': 1450, 'harvard': 2687, 'disguised': 1659, 'welcome': 6497, 'willy': 6589, 'snaps': 5427, 'offer': 4127, 'join': 3165, 'undated': 6268, 'drunk': 1781, 'seats': 5113, 'we-we-we': 6475, "where's": 6537, 'distract': 1673, 'juan': 3176, 'buddies': 819, 'deal': 1508, 'excuses': 2007, 'tall': 5873, 'cheap': 1049, 'looks': 3511, 'turkey': 6206, 'cueball': 1429, 'spoon': 5565, 'yellow-belly': 6733, 'disturbing': 1677, 'badges': 446, 'smiling': 5405, 'dollars': 1699, 'school': 5076, 'publish': 4629, 'access': 100, 'schabadoo': 5070, 'jägermeister': 3199, 'conditioners': 1269, 'hall': 2638, "knockin'": 3280, 'sleep': 5366, 'twentieth': 6224, 'middle': 3752, 'daughter': 1494, 'stooges': 5690, 'diets': 1615, 'sass': 5038, 'great': 2552, "they've": 5996, 'grease': 2551, 'deer': 1533, 'hah': 2626, 'dallas': 1463, 'irrelevant': 3078, 'based': 507, 'has': 2690, 'neat': 3975, 'oooh': 4170, 'demand': 1556, "lovers'": 3546, "'evening": 10, "tv's": 6214, 'zinged': 6775, "stinkin'": 5676, 'game': 2396, 'myself': 3935, 'french': 2323, 'camera': 914, 'text': 5957, 'society_matron:': 5462, 'tha': 5960, 'lucius:': 3557, 'hillary': 2789, "fans'll": 2083, 'ambrose': 247, 'teriyaki': 5938, 'male_inspector:': 3612, 'tv_father:': 6218, 'sweden': 5818, 'kucinich': 3300, 'uh-huh': 6251, 'wearing': 6481, "must've": 3929, 'groveling': 2576, 'clock': 1164, 'guard': 2589, 'wednesday': 6487, 'pointedly': 4467, 'wraps': 6688, 'reopen': 4845, 'failure': 2062, 'grieving': 2562, 'gator:': 2413, 'margarita': 3644, 'maiden': 3599, 'strolled': 5725, 'value': 6339, 'compare': 1242, 'forget': 2275, 'pulls': 4644, 'tough': 6125, 'killjoy': 3248, 'reasonable': 4768, 'buying': 882, "mother's": 3875, 'aside': 359, 'charge': 1035, 'playing': 4440, 'clothes': 1172, 'hubub': 2896, 'xx': 6707, 'specified': 5532, 'additional-seating-capacity': 124, 'industry': 2998, 'monorails': 3846, 'bothered': 715, 'marjorie': 3650, 'kay': 3209, 'lend': 3384, 'tipsy': 6071, 'hawking:': 2706, 'exited': 2013, 'lovely': 3543, 'homesick': 2840, 'ninety-nine': 4035, 'bathing': 514, 'products': 4595, 'both': 714, 'those': 6021, 'president': 4547, 'andalay': 267, 'experienced': 2020, 'accelerating': 95, '_hooper:': 65, 'rig': 4898, 'sinister': 5318, 'shows': 5262, 'appreciate': 320, 'real': 4761, 'plus': 4456, 'dials': 1600, 'exchanged': 2001, 'chick': 1074, 'floating': 2227, 'dna': 1685, 'sheepish': 5214, 'cars': 965, 'unsourced': 6305, 'airport': 196, 'noises': 4048, 'low-life': 3550, 'arab_man:': 329, 'piece': 4387, 'quarry': 4672, 'telling': 5924, 'troubles': 6183, 'flying': 2239, 'dateline:': 1492, 'mindless': 3773, 'ninety-seven': 4036, 'wear': 6479, 'whaaa': 6509, 'blank': 641, 'natured': 3966, 'nickels': 4024, 'numeral': 4091, 'wrecking': 6690, 'ahem': 185, 'broom': 801, 'language': 3327, 'clear': 1150, 'roy': 4954, 'congoleum': 1279, 'hairs': 2631, "soakin's": 5449, 'recap:': 4773, 'wantcha': 6426, 'suspiciously': 5807, 'sidelines': 5284, 'duel': 1791, 'hunger': 2910, 'cletus_spuckler:': 1156, 'sitting': 5335, 'johnny': 3163, 'mccarthy': 3686, 'dean': 1513, 'kadlubowski': 3202, 'gone': 2507, 'tongue': 6102, 'fat-free': 2094, 'violations': 6373, 'bless': 647, 'mouth': 3883, 'blame': 639, 'cruiser': 1419, "patrick's": 4294, 'twelveball': 6223, "football's": 2264, 'most': 3869, 'clearing': 1151, 'deliberately': 1545, 'hexa-': 2771, 'rat-like': 4739, 'souvenir': 5515, "who'll": 6555, 'guiltily': 2596, 'weep': 6492, 'tire': 6072, 'maitre': 3603, 'musketeers': 3925, 'irs': 3079, 'wishes': 6618, 'backward': 441, 'sun': 5770, 'arm-pittish': 342, '__quotation_mark__': 58, 'diamond': 1601, 'superhero': 5780, 'repay': 4847, 'boned': 689, 'leave': 3365, 'pretends': 4559, 'relative': 4809, 'flayvin': 2217, 'experience': 2019, 'taught': 5892, "choosin'": 1104, 'cozies': 1369, 'size': 5343, 'samples': 5026, 'though': 6022, 'christian': 1108, 'results': 4872, "drexel's": 1756, 'dumbest': 1807, 'undies': 6278, 'noggin': 4046, 'pickle': 4380, 'perfected': 4339, 'attention': 388, 'until': 6306, 'step': 5659, 'mob': 3814, 'onion': 4161, 'watching': 6454, 'quimby_#2:': 4687, 'realize': 4763, 'used': 6325, "we've": 6474, 'bits': 632, 'managing': 3631, 'light': 3427, 'ad': 121, 'truck': 6187, "tootin'": 6113, 'priest': 4571, 'spit': 5551, 'locked': 3492, 'hibbert': 2775, 'jobs': 3156, 'found': 2296, 'bunch': 842, 'eh': 1871, 'door': 1720, 'haiti': 2632, 'cloudy': 1175, 'competitive': 1247, 'legs': 3379, 'spamming': 5520, 'yello': 6731, 'jacksons': 3111, 'champion': 1019, 'pour': 4515, 'sharity': 5205, 'ate': 381, 'gheet': 2452, 'dumbass': 1805, 'go': 2494, 'sabermetrics': 4994, 'shove': 5253, 'buttons': 877, 'regret': 4802, 'reserved': 4861, 'shriners': 5266, 'unlike': 6299, 'darjeeling': 1481, 'marvin': 3660, 'swine': 5832, 'knows': 3287, 'tastes': 5888, 'jerk-ass': 3139, 'drivers': 1767, 'sooo': 5497, 'hammy': 2643, 'agent_miller:': 170, 'wars': 6439, 'forty': 2290, 'improv': 2972, 'faulkner': 2101, 'y': 6708, 'victim': 6362, 'against': 163, 'inside': 3026, 'alphabet': 232, 'breakfast': 764, 'cotton': 1334, 'linda': 3441, 'tow': 6127, 'tow-joes': 6128, "smokin'_joe_frazier:": 5412, 'boyfriend': 740, 'harder': 2677, 'eva': 1970, 'utility': 6332, "ain't": 194, 'eco-fraud': 1849, 'learned': 3361, 'o': 4096, 'slice': 5371, 'wikipedia': 6581, 'slurred': 5389, 'warned': 6435, 'yo': 6743, 'terrorizing': 5948, 'walther_hotenhoffer:': 6420, 'germs': 2439, 'same': 5024, 'carll': 956, "should've": 5247, 'gorgeous': 2518, 'teams': 5904, 'bon': 686, 'test-': 5950, 'commit': 1235, 'rubbed': 4960, 'susie-q': 5802, 'head-gunk': 2713, 'loved': 3540, 'restless': 4869, 'hurts': 2919, 'state': 5631, 'appeals': 311, "sittin'": 5334, 'business': 864, 'reluctantly': 4821, 'nervously': 4003, 'apulina': 327, '100': 28, 'waist': 6402, 'less': 3395, 'life-threatening': 3419, 'dazed': 1501, "i'd'a": 2929, 'is': 3080, 'unbelievable': 6263, 'coal': 1182, 'beneath': 590, 'mic': 3740, 'film': 2163, 'glowers': 2489, '__question_mark__': 57, 'senators:': 5155, 'fledgling': 2220, 'solely': 5469, 'bragging': 746, 'hitchhike': 2801, 'stiffening': 5670, 'checks': 1057, 'clown-like': 1177, 'museum': 3921, 'arse': 350, 'bill': 615, 'renee': 4836, 'businessman_#2:': 866, 'dime': 1624, 'ears': 1831, 'themselves': 5981, 'error': 1958, 'changed': 1024, 'boxcar': 731, 'counter': 1342, 'snapping': 5426, 'amid': 255, 'items': 3096, 'cheered': 1059, 'disguise': 1658, 'catch': 977, 'legoland': 3378, 'yelling': 6730, 'whup': 6567, 'archaeologist': 331, 'courteous': 1355, 'saved': 5051, 'knock': 3277, 'understood': 6275, 'comment': 1233, 'broken:': 797, 'collette:': 1210, 'grimly': 2565, 'opening': 4176, 'enemies': 1925, 'grenky': 2558, '7g': 48, 'shelbyville': 5217, "somethin':": 5483, 'owes': 4221, 'patting': 4302, 'testing': 5954, 'augustus': 398, 'soaking': 5450, 'gin': 2466, 'resigned': 4862, 'shesh': 5221, 'manage': 3628, 'busiest': 863, 'five': 2196, 'flexible': 2223, 'ha-ha': 2620, 'mixed': 3804, "couldn't": 1340, 'cocking': 1190, 'gasps': 2411, 'righ': 4899, 'bloodball': 662, 'bills': 621, 'dungeon': 1811, "somethin's": 5484, 'change': 1023, "don'tcha": 1706, 'therefore': 5989, 'modest': 3821, 'normal': 4063, 'donor': 1713, 'purse': 4653, 'brotherhood': 804, 'hand': 2645, 'sweeter': 5820, 'here-here-here': 2765, 'apology': 307, 'sec_agent_#1:': 5115, "tv'll": 6213, 'browns': 809, 'presto:': 4555, 'dying': 1817, 'fruit': 2351, 'womb': 6640, 'learn': 3360, 'upn': 6314, 'ironed': 3076, 'annoyed': 280, 'rebuttal': 4771, 'impress': 2970, 'cappuccino': 935, 'shipment': 5226, 'greetings': 2556, 'barney-guarding': 490, 'mommy': 3839, 'checking': 1056, 'only': 4164, "o'problem": 4099, 'washed': 6442, 'screw': 5096, 'gotta': 2523, "dog's": 1692, 'mines': 3775, 'fox_mulder:': 2306, 'severe': 5179, 'hemorrhage-amundo': 2759, 'certainly': 1007, 'sniffles': 5435, 'kermit': 3226, 'bowie': 726, 'steampunk': 5653, 'dae': 1462, 'certified': 1009, '_julius_hibbert:': 66, 'hootie': 2855, 'chips': 1093, 'effect': 1863, 'sanitary': 5031, 'fights': 2153, 'ralph': 4725, 'ha': 2619, 'wife-swapping': 6576, 'bears': 531, 'beached': 523, 'patty': 4303, 'natural': 3963, 'gently': 2433, 'everyone': 1982, 'bright': 781, 'passenger': 4284, 'christmas': 1109, 'americans': 253, 'window': 6597, 'steal': 5647, 'starting': 5625, 'sickened': 5278, 'salvador': 5021, 'contractors': 1305, 'floated': 2225, 'sloppy': 5384, 'pitcher': 4410, 'dawning': 1498, 'tidy': 6054, 'stumble': 5735, 'general': 2422, 'radiation': 4698, 'design': 1580, 'investor': 3064, 'regulars': 4805, 'lincoln': 3440, 'mellow': 3717, "town's": 6133, 'lennyy': 3390, 'across': 112, 'into': 3054, 'foam': 2241, 'savvy': 5054, 'tip': 6069, 'possessions': 4507, 'absentmindedly': 89, 'loud': 3531, 'type': 6239, 'inspector': 3030, 'thunder': 6049, 'broncos': 799, 'onassis': 4155, "buffalo's": 823, 'notably': 4070, 'cummerbund': 1432, 'gang': 2400, 'dead': 1506, 'frankie': 2310, 'milk': 3766, 'irish': 3074, 'station': 5634, 'done': 1711, 'i/you': 2939, 'script': 5098, 'nobel': 4042, 'proposition': 4616, 'traffic': 6141, 'fictional': 2143, 'reasons': 4769, 'agent_johnson:': 169, 'ring': 4903, 'shoe': 5230, 'there': 5987, 'rivalry': 4913, 'robot': 4921, 'fingers': 2175, 'wistful': 6621, 'perking': 4344, 'whim': 6541, 'reporter:': 4853, 'after': 159, 'conversion': 1310, 'technical': 5909, 'arrest': 347, 'eurotrash': 1969, 'saturday': 5045, 'astrid': 376, 'amanda': 242, "waitin'": 6405, 'department': 1567, 'dishrag': 1662, 'load': 3480, 'pfft': 4359, 'hide': 2777, 'bit': 629, 'negative': 3987, 'orphan': 4192, '2': 34, 'ratio': 4741, 'kick-ass': 3230, 'routine': 4952, 'williams': 6587, 'scully': 5103, 'hangover': 2661, 'afternoon': 161, 'dear': 1514, 'up': 6309, '/mr': 26, 'homunculus': 2842, 'trainers': 6145, 'customers': 1445, 'las': 3337, 'throws': 6045, 'beefs': 551, 'off': 4122, 'beverage': 605, 'unavailable': 6262, 'realized': 4764, 'housework': 2887, 'befouled': 563, 'steinbrenner': 5657, 'movie': 3888, 'sleigh-horses': 5369, 'keep': 3213, 'invented': 3061, 'cock': 1189, 'stupid': 5737, 'er': 1956, 'yak': 6715, 'realizing': 4765, 'pointy': 4471, 'ointment': 4142, 'hopeful': 2859, 'problems': 4589, 'exhibit': 2011, 'homer_doubles:': 2837, "kid's": 3235, 'throats': 6040, 'continuing': 1302, 'mckinley': 3688, 'relaxing': 4812, 'heavyweight': 2739, 'good': 2510, 'doll': 1696, 'again': 162, "barney's": 489, 'booger': 696, 'goods': 2514, 'thought_bubble_lenny:': 6026, 'sat': 5040, "drawin'": 1743, 'miss': 3792, 'stagy': 5600, "'til": 20, '_babcock:': 62, 'funny': 2370, 'self-centered': 5140, "man'd": 3621, 'talk': 5866, 'ehhhhhhhh': 1874, 'home': 2829, 'comedy': 1224, 'naively': 3945, 'spent': 5541, 'statesmanlike': 5633, 'stern': 5661, 'did': 1607, 'sound': 5505, 'books': 701, 'choose': 1103, 'fellow': 2127, 'labor': 3307, 'recommend': 4779, "how's": 2892, 'pretzel': 4564, 'cards': 944, 'tooth': 6112, 'feast': 2111, "lookin'": 3509, 'bedroom': 547, 'tubman': 6201, 'race': 4696, 'love-matic': 3539, 'pancakes': 4253, 'whaaaa': 6510, 'honeys': 2845, 'lizard': 3477, 'pure': 4652, 'incapable': 2978, 'reciting': 4777, 'heals': 2717, 'vodka': 6380, 'stinger': 5673, "father's": 2098, 'poorer': 4492, 'vicious': 6361, 'surprise': 5796, 'mock': 3816, 'happy': 2674, 'wonderful': 6646, 'alter': 236, 'territorial': 5946, 'edelbrock': 1853, 'ooh': 4168, 'singing': 5313, 'lumpa': 3564, 'churchy': 1126, 'watashi': 6450, 'computer': 1260, 'soap': 5451, 'authorized': 402, 'direction': 1635, "seein'": 5127, 'reward': 4880, 'mickey': 3745, "'s": 19, 'louie:': 3534, 'finally': 2167, 'terrified': 5944, "smokin'": 5411, 'round': 4949, 'sneering': 5431, 'm': 3574, 'ask': 360, 'rich': 4887, 'jailbird': 3116, 'complete': 1250, 'bullet-proof': 832, 'charm': 1041, "costume's": 1333, 'assent': 368, "drivin'": 1769, 'shoulder': 5249, 'smug': 5415, 'ruuuule': 4988, 'handoff': 2651, 'eggs': 1869, 'flips': 2224, 'disgraceful': 1656, 'other_book_club_member:': 4196, 'rhyme': 4884, 'proves': 4624, 'selection': 5137, 'sees': 5134, 'kicks': 3233, 'presided': 4546, 'ya': 6713, 'drummer': 1780, 'price': 4566, 'hears': 2724, 'banned': 468, 'selma_bouvier:': 5149, 'whaddya': 6512, 'introduce': 3059, 'gas': 2408, 'pudgy': 4632, 'bolting': 685, 'steam': 5650, 'soft': 5465, 'daaaaad': 1457, 'supplying': 5787, 'clinton': 1161, 'blind': 651, 'eyesore': 2046, 'vengeance': 6348, 'banquet': 470, 'photographer': 4371, 'bully': 834, 'coincidentally': 1201, 'happen': 2667, 'cutting': 1451, 'although': 238, "bar's": 473, '__period__': 56, "sippin'": 5321, 'night-crawlers': 4029, 'yoo': 6746, 'scientists': 5081, 'won': 6642, 'and': 263, 'royal': 4955, 'met': 3732, 'pretend': 4557, 'bold': 684, 'candy': 924, 'manjula_nahasapeemapetilon:': 3638, 'company': 1241, 'became': 541, 'emphasis': 1910, 'runaway': 4978, 'bread': 760, 'refund': 4800, 'admitting': 135, 'loser': 3519, 'failed': 2061, 'poem': 4460, 'misconstrue': 3789, 'upbeat': 6311, 'evergreen': 1978, 'bartending': 503, 'person': 4350, 'lose': 3518, 'gentlemen': 2431, 'patterns': 4301, 'fact': 2056, 'hosting': 2874, "dimwit's": 1627, 'meaningful': 3697, 'smells': 5399, 'ah-ha': 183, 'jumping': 3190, 'normals': 4064, 'brace': 743, 'now': 4082, 'steamed': 5651, 'sentimonies': 5162, "fightin'": 2151, "collector's": 1208, 'cents': 1002, 'patty_bouvier:': 4304, 'kinds': 3255, 'getaway': 2443, 'b-day': 428, 'inspire': 3031, 'done:': 1712, 'rain': 4715, 'midnight': 3755, 'guilt': 2595, 'branding': 753, '2nd_voice_on_transmitter:': 37, 'quietly': 4685, "wouldn't": 6680, 'guns': 2606, 'arrested:': 348, 'dude': 1789, 'like': 3432, 'sex': 5180, 'temper': 5927, 'practically': 4522, 'accepting': 99, 'gutenberg': 2611, 'watered-down': 6457, 'surprised/thrilled': 5798, 'aisle': 197, 'expecting': 2016, 'cell-ee': 999, '10:15': 29, 'yap': 6717, 'holds': 2820, 'crushed': 1423, 'tolerance': 6091, 'vacuum': 6335, 'missing': 3795, "weren't": 6504, 'horses': 2868, 'snotty': 5442, 'weary': 6483, 'second': 5117, 'uses': 6326, 'sweet': 5819, 'solved': 5472, "show's": 5255, 'caused': 987, 'sunglasses': 5772, 'bar': 472, 'alls': 225, 'around': 345, 'vulnerable': 6393, 'women': 6641, 'micronesian': 3747, 'feet': 2121, 'ehhhhhhhhh': 1875, 'whistling': 6550, 'engraved': 1933, 'poin-dexterous': 4464, 'however': 2893, 'ripper': 4910, 'pond': 4488, 'radiator': 4699, 'thinking': 6004, 'martini': 3658, 'inexorable': 3001, 'doppler': 1722, 'rubs': 4961, "poisonin'": 4473, 'cheesecake': 1067, 'clips': 1163, 'cruise': 1418, 'specific': 5531, 'depressed': 1572, 'honest': 2843, "robbin'": 4919, 'war': 6429, "somebody's": 5476, 'alva': 239, 'staying': 5644, 'broke': 795, 'swishkabobs': 5835, 'fishing': 2192, 'rainier_wolfcastle:': 4719, 'might': 3756, 'não': 4095, 'temporarily': 5930, 'plans': 4425, 'plan': 4419, 'hungry': 2911, 'blinds': 653, 'fridge': 2331, 'academy': 94, 'marriage': 3654, 'kool': 3294, 'earlier': 1827, 'know': 3282, 'teenage': 5913, 'b': 426, 'longer': 3502, 'waltz': 6421, 'log': 3496, 'closet': 1170, 'annus': 283, 'rice': 4886, 'twenty-six': 6229, 'gamble': 2394, 'burglary': 847, 'mobile': 3815, 'rounds': 4951, 'abe': 81, 'car:': 941, 'betrayed': 598, 'phrase': 4373, 'blade': 638, 'deserve': 1579, 'squeeze': 5586, 'causes': 988, 'rocks': 4924, 'hawaii': 2704, 'putting': 4665, 'data': 1489, 'contemplates': 1296, 'short_man:': 5241, 'alright': 234, 'felt': 2129, 'piano': 4376, "challengin'": 1016, 'hop': 2856, "england's": 1931, 'urinal': 6321, 'fast': 2089, 'pointed': 4466, 'usually': 6330, 'it:': 3092, "cupid's": 1435, 'jackass': 3106, 'stand': 5610, 'ew': 1988, 'self-satisfied': 5143, 'slipped': 5377, 'simple': 5300, 'corporation': 1325, 'release': 4813, 'lost': 3523, 'chuckles': 1115, 'frazier': 2314, 'mexican_duffman:': 3737, 'jackpot-thief': 3108, 'glorious': 2487, 'chumbawamba': 1121, 'raises': 4722, 'does': 1689, 'reporter': 4852, 'runs': 4981, 'dice': 1604, 'ralph_wiggum:': 4726, 'szyslak': 5847, 'ashtray': 358, 'series': 5165, "listenin'": 3462, 'cheryl': 1069, 'conference': 1272, "messin'": 3731, 'peeved': 4322, 'th-th-th-the': 5959, "bein'": 573, 'usual': 6329, 'german': 2436, 'bloodiest': 664, 'flaming': 2208, 'stab': 5593, 'sighs': 5290, 'various': 6345, 'neighboreeno': 3990, 'argue': 335, 'suru': 5800, 'co-sign': 1180, 'right': 4900, 'frenchman': 2324, 'whip': 6544, 'sea': 5106, "america's": 251, 'skydiving': 5358, 'gag': 2389, 'young': 6753, 'politicians': 4484, 'necessary': 3977, 'tavern': 5894, 'foibles': 2244, "washin'": 6444, 'rife': 4897, 'snatch': 5428, 'simp-sonnnn': 5299, 'statistician': 5636, 'slot': 5385, 'lame': 3319, 'ferry': 2136, 'decency': 1519, 'thinks': 6005, 'cuff': 1430, 'belly': 582, 'frontrunner': 2348, 'nash': 3960, 'haikus': 2627, 'fills': 2162, 'cruel': 1417, 'minus': 3782, 'yes': 6737, 'field': 2144, 'chairman': 1014, 'radio': 4701, 'doy': 1731, 'item': 3095, 'nail': 3943, 'begin': 568, 'excited': 2002, 'pillows': 4393, 'strategy': 5712, 'grocery': 2571, "here's": 2764, 'live': 3471, "countin'": 1344, 'ease': 1833, 'hyahh': 2923, 'nature': 3965, 'hundred': 2908, 'amazing': 244, 'detective_homer_simpson:': 1592, 'raggie': 4708, 'clenched': 1155, "fun's": 2365, 'roz': 4956, 'anonymous': 284, 'scruffy_blogger:': 5100, 'here': 2763, 'nonchalantly': 4053, 'created': 1387, "daughter's": 1495, 'dreamily': 1747, 'drinking': 1761, 'bide': 609, 'civilization': 1136, 'quebec': 4675, 'bust': 867, 'manfred': 3635, 'fighting': 2152, 'example': 1993, 'crap': 1377, 'ref': 4790, 'appreciated': 321, 'joey': 3159, 'bart_simpson:': 499, 'janette': 3122, 'van': 6342, 'maximum': 3676, 'career': 946, 'fireball': 2182, 'fatso': 2099, 'chosen': 1106, 'likes': 3433, 'rector': 4786, 'kissingher': 3266, 'number': 4088, 'come': 1221, 'counterfeit': 1343, 'equal': 1954, 'passes': 4285, "crawlin'": 1382, 'outstanding': 4211, 'pizza': 4412, 'rafter': 4704, 'apron': 324, 'barn': 487, 'wanna': 6424, 'couch': 1335, 'friend:': 2334, 'daddy': 1460, 'innocence': 3018, 'parenting': 4267, 'much': 3897, 'maude': 3674, 'certain': 1006, 'badmouth': 448, 'bye': 886, 'discuss': 1652, 'copy': 1319, 'man_at_bar:': 3625, 'willing': 6588, 'prank': 4525, 'concerned': 1265, 'says': 5059, 'milhouse': 3761, 'determined': 1593, 'radioactive': 4702, 'trashed': 6156, 'easily': 1835, 'resenting': 4859, 'getting': 2447, 'exploiter': 2027, 'sobs': 5458, 'intervention': 3052, 'nibble': 4018, 'gulliver_dark:': 2598, 'call': 905, 'case': 968, 'cheat': 1053, 'engine': 1929, 'ze-ro': 6770, 'jaegermeister': 3114, 'parasol': 4265, 'considering': 1286, 'seething': 5136, 'sight-unseen': 5292, 'stalwart': 5606, 'glasses': 2480, 'word': 6657, 'combines': 1220, 'restroom': 4870, "she's": 5212, 'drop-off': 1774, 'skin': 5349, 'sudden': 5753, 'schemes': 5072, 'attracted': 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'suing': 5764, 'superior': 5781, 'full': 2357, 'young_barfly:': 6754, 'carl': 952, 'wayne': 6466, 'thanking': 5964, 'nice': 4019, 'i-i-i': 2938, 'befriend': 564, 'mona_simpson:': 3840, 'aer': 147, 'flatly': 2216, 'klown': 3270, 'outrageous': 4208, 'sperm': 5542, 'quarterback': 4674, 'straighten': 5704, 'music': 3923, 'startup': 5628, 'sing': 5308, 'extremely': 2037, 'pickles': 4382, 'juke': 3184, 'rusty': 4986, 'yammering': 6716, 'reluctant': 4820, 'tactful': 5856, 'drive': 1764, 'sensible': 5159, 'death': 1516, 'boozy': 708, 'clap': 1141, 'measurements': 3704, 'lugs': 3562, 'booze': 703, 'uncreeped-out': 6267, 'thirteen': 6008, 'beatings': 535, 'directions': 1636, "guy's": 2615, 'insightful': 3027, 'sips': 5322, 'shut': 5271, "queen's": 4677, 'arimasen': 337, 'falling': 2072, 'keeping': 3214, 'child': 1079, 'pus-bucket': 4656, 'elder': 1889, 'fantastic': 2084, "that'll": 5969, 'adequate': 127, '&': 1, 'weird': 6494, 'gregor': 2557, 'hmmm': 2808, 'every': 1979, 'bunion': 843, 'man': 3620, "children's": 1082, 'alec_baldwin:': 207, 'pews': 4358, 'crumble': 1420, "she'll": 5211, "we're": 6473, 'carl_carlson:': 955, 'aunt': 399, 'upon': 6315, 'woozy': 6656, 'fabulous': 2049, 'corkscrew': 1320, 'sob': 5453, 'happens': 2669, 'lenny_leonard:': 3389, 'wish-meat': 6617, "cashin'": 971, 'wake': 6407, "thing's": 5999, 'worked': 6662, 'taxi': 5897, 'burning': 851, 'buttocks': 874, 'riding': 4896, 'bucks': 817, 'tabs': 5855, 'rueful': 4964, 'eleven': 1894, 'leaving': 3367, 'dumb': 1803, 'intruding': 3060, 'manuel': 3640, 'dizer': 1683, 'carefully': 948, 'satisfied': 5044, 'tied': 6056, 'youngsters': 6758, 'administration': 130, 'moe-lennium': 3829, 'rip-off': 4907, 'palm': 4251, 'asking': 363, 'sternly': 5662, 'puts': 4663, 'montrer': 3852, 'handler': 2649, 'carney': 960, 'flame': 2206, 'wipes': 6611, 'knocked': 3279, 'wrap': 6686, 'streetlights': 5716, 'end': 1920, 'awww': 422, 'fit': 2195, 'apply': 318, 'presumir': 4556, 'defected': 1535, 'bring': 785, 'fortune': 2289, 'seductive': 5125, 'innocent': 3019, 'tv_announcer:': 6216, 'mission': 3796, 'meaningfully': 3698, 'spinning': 5548, 'unsafe': 6303, 'grade': 2531, 'ding-a-ding-ding-ding-ding-ding-ding': 1629, 'ihop': 2956, 'group': 2575, 'wiggum': 6579, 'limited': 3438, 'fire': 2180, 'sitcom': 5331, 'menlo': 3726, 'reynolds': 4882, "talkin'": 5871, 'hippies': 2794, 'windowshade': 6598, "hasn't": 2691, 'beanbag': 526, 'moe-near-now': 3830, 'movies': 3889, 'hoax': 2814, 'bonding': 688, 'rome': 4934, "usin'": 6327, 'magazine': 3591, 'fdic': 2109, 'replace': 4850, 'pridesters:': 4570, 'intention': 3046, 'confidence': 1274, 'read:': 4754, 'spare': 5522, 'detective': 1591, 'guide': 2594, 'carnival': 961, 'looser': 3513, 'two-thirds-empty': 6237, 'rub': 4958, 'i': 2927, 'shareholder': 5202, 'wash': 6441, 'purveyor': 4655, 'answered': 287, 'nucular': 4085, 'wolveriskey': 6636, 'suave': 5741, 'voodoo': 6387, 'sweetest': 5821, 'de-scramble': 1503, 'w': 6394, 'haws': 2707, 'dry': 1785, 'cab': 892, 'table': 5851, 'quick-like': 4682, 'picky': 4383, 'winces': 6591, 'sign': 5293, 'coffee': 1197, 'total': 6118, 'pigs': 4389, 'fortensky': 2287, 'power': 4518, "game's": 2397, 'stonewall': 5688, 'tell': 5922, 'diminish': 1625, 'near': 3973, 'pepto-bismol': 4334, 'japanese': 3123, "ladies'": 3311, 'chum': 1120, 'suspicious': 5806, 'sounded': 5506, 'shutting': 5273, 'meal': 3692, 'other_player:': 4197, "c'mere": 889, 'am': 241, 'luxury': 3572, 'wound': 6682, 'investigating': 3062, 'least': 3363, 'gabriel:': 2388, 'plum': 4454, 'wuss': 6702, "'bout": 3, 'safely': 5005, 'sadistic_barfly:': 5001, 'crony': 1405, 'skunk': 5356, 'figure': 2154, 'ridiculous': 4894, 'bedridden': 546, 'knees': 3272, 'proper': 4613, 'suffering': 5759, 'beauty': 540, 'fastest': 2092, 'insensitive': 3023, 'cute': 1448, 'agents': 171, 'recently': 4775, 'wudgy': 6701, 'mind': 3771, 'names': 3951, 'skinheads': 5350, 'fumigated': 2363, 'prove': 4623, 'tigers': 6058, 'television': 5921, 'george': 2435, 'milhouse_van_houten:': 3762, 'len-ny': 3383, 'hike': 2787, 'oak': 4101, 'noosey': 4059, 'entirely': 1949, 'championship': 1020, '__exclamation_mark__': 54, 'options': 4182, 'innocuous': 3020, 'already': 233, 'die-hard': 1611, 'fausto': 2103, 'perplexed': 4348, 'oh-ho': 4135, 'headhunters': 2714, 'madman': 3588, 'evil': 1986, 'pursue': 4654, 'dessert': 1587, 'newly-published': 4010, 'accusing': 106, 'u': 6243, 'horrors': 2867, 'tv_wife:': 6220, 'professor': 4597, 'extreme': 2036, 'minute': 3783, 'designated': 1581, 'promised': 4607, 'yet': 6740, 'ninety-six': 4037, 'louisiana': 3535, 'gimmicks': 2465, 'permanent': 4345, "fendin'": 2135, "wouldn't-a": 6681, 'pepsi': 4333, 'anarchy': 260, 'menace': 3724, "takin'": 5863, 'slim': 5375, 'superdad': 5779, 'prize': 4583, 'capuchin': 938, 'gossipy': 2520, 'squadron': 5581, 'somehow': 5478, 'white_rabbit:': 6552, 'delicious': 1548, 'rent': 4842, 'finger': 2174, 'pub': 4627, 'write': 6693, 'softer': 5466, 'moe-clone': 3826, 'poor': 4491, 'moonlight': 3856, 'fool': 2259, 'kissing': 3265, 'coins': 1203, 'guts': 2612, 'eat': 1841, 'register': 4801, 'motto': 3878, 'tree': 6162, 'family-owned': 2077, 'rump': 4976, 'fancy': 2081, 'marshmallow': 3657, 'creates': 1388, 'friendly': 2335, 'till': 6060, 'badly': 447, 'polenta': 4478, 'simpsons': 5303, "chewin'": 1072, 'jets': 3147, 'listen': 3460, 'lobster-based': 3487, 'half-back': 2634, 'triumphantly': 6177, 'bumbling': 837, 'gallon': 2392, 'lot': 3524, 'totalitarians': 6119, 'peter_buck:': 4356, 'sisters': 5327, 'h': 2618, 'poulet': 4513, 'greystash': 2560, 'train': 6144, 'card': 943, 'husband': 2920, 'avec': 406, 'city': 1132, 'sister-in-law': 5326, 'center': 1001, 'abercrombie': 82, 'sneaky': 5430, 'tang': 5874, 'mystery': 3936, "poundin'": 4514, 'perverse': 4352, 'frosty': 2349, 'peter': 4355, 'top': 6114, 'castle': 973, 'moonnnnnnnn': 3857, "g'on": 2385, 'east': 1836, 'corpses': 1326, 'jumps': 3191, 'bottle': 716, 'seymour_skinner:': 5185, "'im": 11, 'sandwich': 5028, 'wonder': 6644, 'in': 2974, 'carpet': 964, 'stamp': 5607, 'letter': 3400, 'said:': 5013, 'teeth': 5917, 'chuckle': 1114, 'so-called': 5446, 'thirty-thousand': 6012, 'versus': 6355, 'authenticity': 400, 'compared': 1243, 'ping-pong': 4400, 'securities': 5123, 'die': 1610, 'woodchucks': 6652, 'football_announcer:': 2265, 'stones': 5687, 'mid-conversation': 3750, 'germany': 2438, 'longest': 3503, 'knuckles': 3289, 'ow': 4219, "we'd": 6471, 'indignant': 2997, 'rookie': 4939, 'committee': 1236, 'sexy': 5183, 'before': 562, 'excavating': 1996, 'volunteer': 6385, 'voters': 6390, "rustlin'": 4985, 'nominated': 4049, 'flaking': 2205, 'pardon': 4266, 'whaddaya': 6511, "carl's": 953, 'lights': 3431, 'suicide': 5763, 'furious': 2371, 'stage': 5597, 'driver': 1766, 'in-in-in': 2976, 'you-need-man': 6752, 'cherry': 1068, 'aged_moe:': 166, 'eu': 1967, 'suddenly': 5754, 'month': 3850, 'rub-a-dub': 4959, 'contract': 1304, 'dishonor': 1661, 'allegiance': 220, 'garbage': 2402, 'canyonero': 930, 'mags': 3596, 'land': 3321, "bart's": 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'sweetie': 5823, 'nemo': 3998, 'wood': 6651, 'took': 6110, 'ayyy': 424, 'wings': 6602, 'warren': 6438, 'starve': 5629, 'religion': 4818, 'paints': 4246, 'give': 2473, 'smooth': 5413, 'wad': 6398, 'heatherton': 2733, 'right-handed': 4901, 'space-time': 5517, 'liver': 3473, "smackin'": 5392, 'schmoe': 5074, "they'll": 5994, 'dig': 1619, 'lofty': 3495, 'guzzles': 2617, 'eyeballs': 2042, 'resolution': 4864, 'dash': 1487, 'pretentious_rat_lover:': 4560, 'duff_announcer:': 1795, 'bridge': 777, 'pipes': 4405, 'alcoholic': 204, 'marge_simpson:': 3647, 'ninety-eight': 4034, 'air': 195, "aren't": 333, 'presently': 4544, 'pugilist': 4635, 'i-i': 2935, 'camp': 916, 'puffy': 4634, 'specializes': 5528, 'robin': 4920, 'specials': 5529, 'haircuts': 2630, 'y-you': 6712, 'kid': 3234, 'charter': 1043, 'inspiring': 3033, 'cozy': 1370, '/': 25, 'bindle': 623, 'clone': 1165, "somethin'": 5482, 'large': 3332, 'justice': 3197, 'heartily': 2728, 'disapproving': 1647, 'umm': 6256, 'emporium': 1913, "round's": 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6132, 'hooch': 2849, 'turlet': 6207, 'scarf': 5065, 'thru': 6046, 'getup': 2449, 'ribbon': 4885, 'urine': 6322, 'interesting': 3048, 'sacrilicious': 4998, 'forget-me-shot': 2277, 'bobo': 680, 'impressed': 2971, 'calculate': 901, 'appointment': 319, 'cockroach': 1191, 'squishee': 5591, 'delicate': 1546, 'gil_gunderson:': 2462, "hobo's": 2816, 'c': 888, 'wangs': 6423, 'african': 157, 'distributor': 1675, 'dentist': 1564, 'tells': 5925, 'november': 4081, 'post-suicide': 4509, 'beer:': 559, 'john': 3162, 'hearse': 2725, 'tale': 5865, 'gin-slingers': 2467, 'babar': 429, 'gary_chalmers:': 2407, 'allow': 222, 'stealings': 5649, 'mitts': 3802, 'chinese_restaurateur:': 1088, 'iddilies': 2943, 'yells': 6734, 'bought': 721, "wino's": 6607, 'unattended': 6260, 'maggie': 3592, 'ugliest': 6247, 'safety': 5007, 'bush': 861, 'demo': 1557, 'jimmy': 3153, 'otherwise': 4199, 'smurfs': 5418, 'tuborg': 6202, 'bathroom': 515, 'fonda': 2252, 'jeers': 3132, 'midge:': 3754, 'hold': 2818, 'clean': 1145, 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6556, 'warm_female_voice:': 6431, 'ga': 2386, 'flash': 2211, "idea's": 2945, 'rims': 4902, 'swatch': 5812, 'threatening': 6034, 'citizens': 1131, 'self-esteem': 5141, 'voyager': 6391, 'carlson': 958, "tramp's": 6149, 'encouraging': 1919, 'all': 215, 'sticking-place': 5669, 'nap': 3953, 'lap': 3329, 'tradition': 6139, 'statues': 5639, 'sponsoring': 5563, 'corkscrews': 1321, 'glass': 2479, 'jebediah': 3131, 'viva': 6379, 'blood': 660, 'brassiest': 756, 'poetics': 4462, 'shaky': 5195, 'helped': 2753, 'oof': 4167, 'background': 439, 'passports': 4287, 'mariah': 3649, "men's": 3722, 'nine': 4032, 'impeach': 2966, 'flash-fry': 2212, '3': 38, "people's": 4328, 'portuguese': 4504, 'hugh:': 2902, "renovatin'": 4840, 'hey': 2772, 'gay': 2415, 'unfresh': 6286, 'neon': 3999, 'flustered': 2237, 'cheaper': 1051, "this'll": 6015, 'intoxicants': 3055, 'grabbing': 2528, "'n'": 14, 'treats': 6161, 'awe': 414, 'oils': 4141, 'worth': 6676, "kiddin'": 3236, 'icy': 2942, 'injury': 3016, 'thanksgiving': 5966, 'marry': 3656, 'sedaris': 5124, 'moxie': 3891, 'prayers': 4527, 'extinguishers': 2033, 'kazoo': 3210, 'dreamy': 1749, 'spit-backs': 5552, 'bowled': 728, 'stewart': 5664, 'lungs': 3565, 'lives': 3474, 'thoughtless': 6029, 'orifice': 4190, 'backgammon': 438, 'owe': 4220, 'adult_bart:': 140, 'slobbo': 5379, 'minister': 3779, 'chug': 1117, 'democrats': 1559, 'remote': 4834, 'wiggle-frowns': 6578, 'alpha-crow': 231, 'cowboys': 1366, "s'okay": 4991, 'ali': 210, 'obsessive-compulsive': 4106, 'shoes': 5231, 'reach': 4747, 'listening': 3463, 'run': 4977, 'grateful': 2547, 'stillwater:': 5672, 'eyeing': 2044, 'beginning': 569, "'roids": 17, 'sunday': 5771, 'filth': 2164, 'store': 5696, 'caught': 984, 'mabel': 3578, 'anyhoo': 297, 'hate': 2693, 'scent': 5069, 'suspect': 5803, 'own': 4222, 'way:': 6463, 'grind': 2568, 'dozen': 1732, 'bon-bons': 687, 'day': 1499, 'liable': 3405, 'hooters': 2854, 'witches': 6622, 'snake_jailbird:': 5423, 'pained': 4240, 'remembering': 4828, 'barf': 481, 'calm': 910, 'michael_stipe:': 3743, '__semicolon__': 61, 'hollowed-out': 2825, 'represent': 4854, 'melodramatic': 3718, 'score': 5084, 'p-k': 4228, 'reactions': 4752, "beggin'": 566, 'ding-a-ding-ding-a-ding-ding': 1628, "tatum'll": 5891, 'hunka': 2912, 'tv_daughter:': 6217, 'stretch': 5717, 'months': 3851, 'depository': 1570, 'panicked': 4254, 'growing': 2578, 't-shirt': 5848, 'anxious': 294, 'homie': 2841, 'henry': 2761, 'conditioner': 1268, 'amends': 249, 'depressant': 1571, 'banquo': 471, 'urge': 6320, 'faded': 2059, 'sports': 5567, 'enjoys': 1938, 'lush': 3569, 'ecru': 1850, 'scotch': 5088, 'radishes': 4703, 'senators': 5154, "hell's": 2748, 'sideshow_mel:': 5287, 'poured': 4516, 'moment': 3837, 'monkeyshines': 3845, 'nonchalant': 4052, 'baby': 432, 'overturned': 4218, "valentine's": 6336, 'alky': 214, 'peppy': 4332, 'fritz:': 2343, 'unfair': 6282, 'six': 5337, 'ruby-studded': 4962, 'pushes': 4658, 'principal': 4576, 'france': 2308, 'men:': 3723, 'sings': 5317, 'cab_driver:': 893, 'island': 3082, 'play': 4433, 'majority': 3605, 'satisfaction': 5043, 'soul-crushing': 5504, 'focus': 2242, 'furry': 2374, 'history': 2799, 'fires': 2184, 'ooo': 4169, 'helllp': 2750, 'twenty-nine': 6228, 'jogging': 3161, '1973': 32, 'fry': 2353, 'angel': 271, 'stained-glass': 5601, 'shoots': 5236, 'lily-pond': 3434, 'unattractive': 6261, 'wheels': 6530, 'position': 4505, 'brag': 745, 'ear': 1826, 'law': 3349, 'ceremony': 1005, 'accept': 97, 'bar_rag:': 476, 'ominous': 4152, 'birthplace': 628, 'knowing': 3283, 'mushy': 3922, 'delightfully': 1551, 'hilton': 2791, 'diapers': 1603, 'lanes': 3324, 'bathtub': 516, "toot's": 6111, 'small': 5393, 'suit': 5765, 'fears': 2110, 'invisible': 3065, 'share': 5201, 'taunting': 5893, 'string': 5720, 'ons': 4165, 'quality': 4671, 'tomato': 6094, 'infor': 3007, 'vampires': 6341, 'stood': 5689, 'multiple': 3908, 'accent': 96, "ball's": 457, 'frink': 2340, 'lovers': 3545, 'hm': 2805, 'denver': 1565, 'smithers': 5406, 'though:': 6023, 'trip': 6175, 'virtual': 6377, 'dignified': 1621, 'hostages': 2872, 'pal': 4249, 'wobble': 6629, 'forbidden': 2267, 'veteran': 6359, 'bake': 454, 'survive': 5801, 'cigarette': 1127, 'further': 2375, 'western': 6506, 'liar': 3406, 'kang:': 3205, 'dryer': 1786, 'pad': 4232, 'chauffeur:': 1048, 'groans': 2570, 'stirrers': 5680, 'expert': 2022, 'don': 1704, 'fans': 2082, 'plastered': 4430, 'businessman_#1:': 865, 'traitor': 6147, 'according': 103, 'bump': 838, 'whining': 6542, 'five-fifteen': 2197, 'tasimeter': 5886, 'reflected': 4795, 'patriotic': 4295, 'artie_ziff:': 353, 'ees': 1862, 'quotes': 4692, 'accounta': 104, 'one-hour': 4159, 'teenage_bart:': 5915, 'twelve-step': 6222, 'awful': 417, 'curds': 1436, 'agreement': 181, 'bob': 679, 'wiping': 6612, 'balls': 462, "fine-lookin'": 2172, 'planted': 4427, 'grandkids': 2541, 'albeit': 201, 'slogan': 5382, 'jeff': 3134, 'married': 3655, 'skinner': 5351, 'wins': 6608, 'brave': 758, 'wine': 6600, 'lied': 3413, 'stock': 5682, 'canoodling': 927, 'kako:': 3204, 'swig': 5827, 'shorter': 5243, 'trying': 6198, 'tapered': 5881, 'fix': 2198, 'frustrated': 2352, 'bet': 596, 'forty-nine': 2292, 'cadillac': 895, 'u2:': 6244, 'pick': 4377, 'energy': 1927, "neighbor's": 3989, 'crimes': 1399, 'rainforest': 4717, 'meyerhof': 3739, 'juice': 3183, 'challenge': 1015, 'pathetic': 4292, 'moe-heads': 3828, 'linda_ronstadt:': 3442, 'rockers': 4923, 'powers': 4521, 'optimistic': 4180, 'penny': 4326, 'important': 2968, 'listens': 3464, 'leftover': 3372, 'spitting': 5555, 'admit': 134, 'make:': 3607, 'shush': 5270, 'ron': 4935, 'offense': 4125, 'muertos': 3899, 'glove': 2488, "spaghetti-o's": 5519, 'bam': 464, 'dan': 1470, 'outlive': 4206, 'inquiries': 3021, 'enthusiasm': 1946, 'incognito': 2983, 'gets': 2445, 'food': 2257, 'beloved': 586, 'indecipherable': 2989, 'glee': 2481, 'hurting': 2918, 'times': 6064, 'doreen:': 1724, 'character': 1033, 'mmm': 3807, 'trees': 6166, '__comma__': 52, 'sucked': 5749, 'root': 4942, 'verticality': 6356, 'goblins': 2497, 'moe-clone:': 3827, 'searching': 5109, 'mmmm': 3809, 'dog': 1691, 'ugh': 6245, 'p': 4227, 'highball': 2783, 'richer': 4890, 'updated': 6312, 'polls': 4486, 'cookies': 1313, 'sprawl': 5572, 'william': 6586, 'impending': 2967, 'newsies': 4012, 'sits': 5333, 'huggenkiss': 2900, ':': 51, 'full-bodied': 2359, 'blimp': 650, 'wishful': 6619, 'acquitted': 110, 'frightened': 2338, 'information': 3009, 'stay': 5640, 'cameras': 915, 'prohibit': 4603, 'tony': 6107, 'machine': 3583, 'robbers': 4918, 'morose': 3866, 'al_gore:': 199, "coffee'll": 1198, 'ned_flanders:': 3982, 'sturdy': 5740, 'heavyset': 2738, 'lecture': 3368, 'voice_on_transmitter:': 6383, 'grrrreetings': 2579, 'crowd:': 1413, 'prime': 4572, 'rupert_murdoch:': 4983, 'slightly': 5374, 'noble': 4043, 'yoink': 6745, 'graveyard': 2550, 'woo-hoo': 6650, 'troy_mcclure:': 6186, 'shoot': 5233, 'composite': 1257, 'bad': 443, 'gluten': 2492, 'pulled': 4641, 'astronaut': 377, "duelin'": 1792, 'lenses': 3392, 'fritz': 2342, 'ma': 3575, 'successful': 5746, 'texas': 5956, 'whether': 6538, 'blend': 646, 'lips': 3451, 'blown': 669, 'accidents': 102, 'read': 4753, 'ugliness': 6248, 'apartment': 305, 'colorado': 1214, 'beer': 555, 'freak': 2315, 'lucinda': 3555, 'quite': 4691, 'jewish': 3149, 'parked': 4271, 'backwards': 442, 'wheel': 6529, 'peach': 4316, 'you': 6747, 'moon': 3854, 'windex': 6596, 'wow': 6684, 'bitter': 633, 'donate': 1707, '1979': 33, 'togetherness': 6085, 'heh': 2741, 'sounds': 5508, 'dropping': 1776, 'buzz': 883, 'bridges': 778, 'wildfever': 6584, 'yep': 6736, 'troy:': 6185, 'fellas': 2126, "tryin'": 6197, 'cheerleaders:': 1063, 'could': 1338, 'ford': 2271, 'heads': 2716, 'defeated': 1534, 'fly': 2238, 'escort': 1960, 'social': 5459, 'allowed': 224, 'convinced': 1311, 'elves:': 1902, 'kisser': 3263, 'professional': 4596, 'doom': 1718, 'miss_lois_pennycandy:': 3793, 'app': 308, 'strategizing': 5711, 'sixteen': 5339, 'over': 4213, 'faceful': 2054, 'tar-paper': 5885, "tony's": 6108, 'seminar': 5151, 'entrance': 1950, 'la': 3304, 'contemplated': 1295, 'slugger': 5387, 'ivana': 3099, 'dang': 1475, 'etc': 1965, 'high-definition': 2781, 'snow': 5444, 'billboard': 617, 'frankenstein': 2309, 'pall': 4250, 'crazy': 1385, 'faced': 2053, 'nicer': 4020, 'grammys': 2536, 'interrupting': 3051, 'choked': 1100, 'speak': 5523, 'date': 1490, 'ivory': 3101, 'birthday': 627, 'abusive': 93, 'busy': 869, 'flashbacks': 2213, 'fine': 2171, 'writer:': 6694, 'snotball': 5441, 'hole': 2821, 'partner': 4278, 'puff': 4633, 'steely-eyed': 5655, 'agree': 180, 'led': 3369, 'chunk': 1122, 'reaction': 4751, 'complaining': 1248, 'stats': 5637, 'slender': 5370, 'para': 4263, 'surprised': 5797, 'brockelstein': 792, 'law-abiding': 3350, 'boring': 709, 'officer': 4129, 'upsetting': 6317, 'shakespeare': 5193, 'workers': 6663, 'midge': 3753, 'advance': 141, 'lock': 3491, 'kindly': 3254, 'newsletter': 4013, 'discussing': 1653, 'road': 4916, 'whatever': 6525, 'punkin': 4651, 'effervescent': 1866, 'weak': 6476, 'naturally': 3964, 'wounds': 6683, 'slow': 5386, 'aggravazes': 174, 'conspiracy': 1290, 'notch': 4071, 'subscriptions': 5744, 'anywhere': 303, 'ingredient': 3013, 'tape': 5880, 'subject': 5743, 'achem': 108, 'killed': 3245, 'died': 1612, "d'ya": 1456, 'work': 6661, 'later': 3341, 'pontiff': 4489, 'twerpy': 6231, 'sistine': 5328, 'barney-shaped_form:': 491, 'popping': 4498, 'tap': 5878, 'lou': 3529, 'find': 2169, 'shrugging': 5267, 'loves': 3547, 'winning': 6605, 'cooler': 1316, 'ball': 456, 'dr': 1733, 'blocked': 658, 'men': 3721, 'excitement': 2003, 'bury': 859, 'license': 3411, 'dame': 1465, 'ivanna': 3100, 'girls': 2472, 'nope': 4060, 'tapestry': 5882, 'strips': 5723, "wait'll": 6404, 'reserve': 4860, 'powered': 4519, 'fevered': 2139, 'night': 4028, 'duffman': 1797, 'pajamas': 4248, 'dizzy': 1684, 'quarter': 4673, 'bed': 544, 'debonair': 1517, "i-i'll": 2936, 'instead': 3036, 'queen': 4676, 'warranty': 6437, "that'd": 5968, 'mad': 3585, 'published': 4630, 'settled': 5175, 'nordiques': 4062, 'eighty-six': 1883, 'sir': 5323, "neat's-foot": 3976, 'chew': 1071, 'navy': 3969, 'compressions': 1258, 'assumed': 374, 'forty-two': 2294, 'out': 4205, 'system': 5846, 'wipe': 6610, 'settlement': 5176, 'evasive': 1971, 'fan': 2079, "nixon's": 4040, "you've": 6751, 'campaign': 917, 'advantage': 142, 'playful': 4437, 'dejected_barfly:': 1540, 'asses': 370, 'almost': 228, 'attractive_woman_#1:': 393, 'sodas': 5464, 'plain': 4417, 'merchants': 3728, 'doors': 1721, 'microwave': 3749, 'w-a-3-q-i-zed': 6395, 'slight': 5373, 'comic': 1228, 'shaggy': 5189, 'ruined': 4968, 're:': 4746, 'whispers': 6548, 'advice': 146, 'easy-going': 1839, 'short': 5240, 'grandé': 2544, 'shoulda': 5248, "it'll": 3090, 'hottest': 2880, 'madonna': 3589, 'pleasure': 4447, 'older': 4148, 'lainie:': 3318, 'treat': 6159, 'gel': 2421, 'boxer': 733, 'begins': 570, 'grienke': 2561, 'wears': 6482, 'telemarketing': 5919, 'beers': 560, 'scratching': 5093, 'aquafresh': 328, 'halloween': 2639, 'marquee': 3653, 'story': 5702, 'reader': 4755, 'hoping': 2860, 'drawer': 1742, 'laughing': 3345, 'chain': 1011, 'ocean': 4116, 'seven': 5178, 'simpson': 5302, 'period': 4343, 'regretted': 4804, 'gary:': 2406, 'thankful': 5963, 'unkempt': 6297, 'belly-aching': 583, 'than': 5961, 'eddie': 1851, 'burns': 852, 'yourself': 6762, 'jukebox_record:': 3186, "department's": 1568, 'guys': 2616, 'shock': 5228, 'tabooger': 5854, 'eighty-seven': 1882, 'audience': 396, 'amber': 245, 'crayola': 1383, 'lloyd': 3478, 'restaurant': 4867, 'kyoto': 3302, 'compliment': 1254, 'enabling': 1915, 'hellhole': 2749, 'confident': 1275, 'single-mindedness': 5316, 'address': 125, 'drift': 1757, 'düff': 1820, 'driveability': 1765, 'positive': 4506, 'dating': 1493, 'committing': 1237, 'joy': 3175, 'opens': 4177, '14': 30, 'hygienically': 2925, 'finance': 2168, 'koholic': 3291, 'amused': 258, 'happened': 2668, 'please/': 4445, 'firing': 2186, 'winston': 6609, 'muffled': 3900, 'amiable': 254, 'glummy': 2491, "jackpot's": 3107, 'set': 5173, 'scrubbing': 5099, 'thoughts': 6030, 'arrange': 346, 'anymore': 299, 'wazoo': 6469, 'thank': 5962, "hadn't": 2624, 'promotion': 4608, 'parents': 4268, "spiffin'": 5545, 'considering:': 1287, 'ummmmmmmmm': 6257, 'whenever': 6534, 'countryman': 1348, 'dum-dum': 1802, 'tearfully': 5906, 'legs:': 3380, 'bret:': 773, 'haw': 2703, 'hidden': 2776, 'bag': 450, 'gentle': 2428, 'microbrew': 3746, 'beating': 534, 'brunswick': 811, 'mafia': 3590, 'sweaty': 5817, 'tapping': 5883, 'woman': 6637, 'percent': 4336, 'selling': 5146, 'sent': 5161, 'worthless': 6677, "'pu": 16, 'facebook': 2052, 'bear': 528, 'living': 3476, 'pyramid': 4669, 'shakes': 5192, 'snake-handler': 5422, 'aims': 193, 'dads': 1461, 'eats': 1846, 'fresh': 2327, 'occupied': 4112, 'dismissive': 1665, 'touchdown': 6122, 'wild': 6582, 'mmm-hmm': 3808, 'life-partner': 3417, 'peeping': 4321, 'wall': 6414, "writin'": 6696, 'alarm': 200, 'divine': 1680, 'brings': 787, 'amber_dempsey:': 246, "one's": 4158, 'hemoglobin': 2758, 'housewife': 2886, 'stirring': 5681, 'pain': 4239, 'teach': 5899, 'children': 1081, 'past': 4288, 'patron_#1:': 4296, 'waylon': 6464, "'morning": 13, 'teenage_homer:': 5916, 'insulin': 3038, 'novelty': 4080, 'feminist': 2133, 'shocked': 5229, '_powers:': 70, 'sour': 5511, 'meteor': 3734, 'compels': 1244, 'barney': 488, 'bottles': 717, 'panicky': 4255, 'kemi': 3217, 'wondered': 6645, 'chapstick': 1031, 'cleaned': 1146, 'mason': 3663, 'edna': 1857, 'meanwhile': 3702, 'mountain': 3880, 'pre-recorded': 4530, 'serum': 5168, 'beeps': 554, 'drawn': 1745, 'illegal': 2957, 'flashing': 2214, 'approval': 323, "'kay-zugg'": 12, 'nameless': 3950, "time's": 6063, 'bigger': 612, 'luckiest': 3559, "coaster's": 1185, 'ground': 2574, 'persia': 4349, 'sense': 5158, "'em": 7, 'triple-sec': 6176, 'material': 3670, 'dignity': 1622, 'jovial': 3174, 'despite': 1586, 'title:': 6075, 'shred': 5263, 'safe': 5003, 'sampler': 5025, 'chilly': 1085, 'virile': 6375, 'expression': 2030, 'win': 6590, "others'": 4198, 'eww': 1989, 'pumping': 4645, 'smelly': 5400, 'remorseful': 4833, 'applicant': 316, 'yourse': 6761, 'goes': 2499, 'borrow': 711, 'adrift': 137, 'officials': 4131, 'choked-up': 1101, 'smallest': 5395, 'represents': 4855, 'wolverines': 6635, 'joey_kramer:': 3160, 'speech': 5534, 'op': 4173, 'musses': 3927, 'fwooof': 2382, 'fl': 2201, 'this:': 6016, 'generally': 2423, 'disappointing': 1645, 'tuna': 6204, 'that': 5967, 'occurs': 4115, 'mahatma': 3597, 'profiling': 4600, 'disaster': 1648, 'insist': 3028, 'pope': 4494, 'maman': 3618, 'crowd': 1412, 'actually': 120, 'gig': 2459, 'clapping': 1142, 'favorite': 2105, 'poisoning': 4474, 'contest': 1300, "i'd": 2928, 'moments': 3838, 'tense': 5935, 'stretches': 5718, 'huddle': 2897, 'puke': 4636, 'costume': 1332, 'coined': 1202, 'wha': 6508, 'march': 3642, 'device': 1596, 'muscle': 3919, 'attractive_woman_#2:': 394, 'cries': 1397, 'windshield': 6599, 'medical': 3709, 'four-drink': 2301, "duff's": 1794, 'shutup': 5274, 'nervous': 4002, 'beings': 575, 'choices:': 1098, "shan't": 5198, 'halvsies': 2640, 'friends': 2336, 'for': 2266, 'tinkle': 6066, 'clams': 1138, 'david': 1496, 'but': 870, 'bachelorette': 434, 'words': 6659, 'taxes': 5896, 'coy': 1367, 'cowboy': 1365, "maggie's": 3593, '6': 45, 'tonic': 6103, "pope's": 4495, 'not': 4069, 'splash': 5556, 'speaking': 5525, 'pantry': 4257, 'shuts': 5272, 'drank': 1739, 'oblongata': 4105, 'trivia': 6178, 'flush-town': 2236, 'chipped': 1091, 'musical': 3924, 'x-men': 6705, 'fired': 2183, 'sick': 5277, 'pronto': 4611, 'mini-dumpsters': 3777, "'topes": 22, 'cushions': 1443, 'jazz': 3129, 'adventure': 143, 'gayer': 2416, 'hoo': 2848, 'rock': 4922, 'enlightened': 1939, 'hans:': 2665, 'admirer': 132, 'classy': 1144, 'valley': 6337, 'brewed': 774, 'crystal': 1425, 'toy': 6135, 'confused': 1278, 'stole': 5684, 'burt_reynolds:': 858, 'yee-ha': 6727, 'supreme': 5792, 'doubt': 1727, 'tones': 6101, 'lifetime': 3422, 'preparation': 4540, 'speed': 5535, 'hers': 2769, 'cooker': 1312, 'nobody': 4044, 'acting': 114, 'blues': 673, 'napkins': 3954, 'wooooo': 6654, 'little_hibbert_girl:': 3469, 'sweater': 5816, 'rat': 4738, 'jerry': 3144, 'devils:': 1597, 'gumbel': 2601, 'actors': 118, "plaster's": 4429, 'three': 6035, 'batmobile': 517, 'closer': 1168, "sat's": 5041, 'baloney': 463, 'smoke': 5408, 'villanova': 6370, 'twenty-two': 6230, 'uh': 6250, 'prompting': 4609, 'widow': 6570, 'one': 4157, 'barkeeps': 486, 'premise': 4538, 'sanitation': 5032, 'ails': 192, 'mimes': 3770, 'buzziness': 884, 'civic': 1134, 'dirt': 1640, 'graves': 2549, 'typing': 6241, 'bell': 581, 'beaumarchais': 537, 'fad': 2058, 'college': 1209, 'darkest': 1483, 'hunter': 2914, 'fainted': 2064, 'heaven': 2735, 'start': 5621, 'crime': 1398, 'hanh': 2663, 'naegle': 3939, "makin'": 3609, 'stupidest': 5738, 'sucker': 5750, 'proud': 4621, 'ticket': 6051, 'multi-national': 3906, 'offa': 4123, 'hoped': 2858, 'sneeze': 5432, 'jig': 3150, 'remember': 4826, 'starla:': 5618, 'liser': 3458, 'barstools': 495, 'young_moe:': 6757, 'problemo': 4588, 'polishing': 4481, 'formico:': 2286, 'heave-ho': 2734, 'without': 6624, 'madison': 3587, 'groan': 2569, 'notorious': 4078, "'round": 18, "mcstagger's": 3690, 'newsweek': 4015, 'greatest': 2553, 'hibachi': 2774, 'nods': 4045, 'showing': 5261, 'class': 1143, 'code': 1196, 'illustrates': 2959, 'd': 1454, 'loaded': 3481, 'racially-diverse': 4697, 'drown': 1778, 'finale': 2166, 'abandon': 79, 'reminded': 4830}

In [8]:
import pickle
int_text = [vocab_to_int[word] for word in text]
pickle.dump((int_text, vocab_to_int, int_to_vocab, token_dict), open('preprocess.p', 'wb'))

In [9]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
# Preprocess Training, Validation, and Testing Data
helper.preprocess_and_save_data(data_dir, token_lookup, create_lookup_tables)

Check Point

This is your first checkpoint. If you ever decide to come back to this notebook or have to restart the notebook, you can start from here. The preprocessed data has been saved to disk.


In [10]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
import numpy as np
import problem_unittests as tests

int_text, vocab_to_int, int_to_vocab, token_dict = helper.load_preprocess()

Build the Neural Network

You'll build the components necessary to build a RNN by implementing the following functions below:

  • get_inputs
  • get_init_cell
  • get_embed
  • build_rnn
  • build_nn
  • get_batches

Check the Version of TensorFlow and Access to GPU


In [11]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer'
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))


TensorFlow Version: 1.0.0
Default GPU Device: /gpu:0

Input

Implement the get_inputs() function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Input text placeholder named "input" using the TF Placeholder name parameter.
  • Targets placeholder
  • Learning Rate placeholder

Return the placeholders in the following the tuple (Input, Targets, LearingRate)


In [12]:
def get_inputs():
    """
    Create TF Placeholders for input, targets, and learning rate.
    :return: Tuple (inputs, targets, learning rate)
    """    
    inputs = tf.placeholder(tf.int32, [None, None], name='input')
    targets = tf.placeholder(tf.int32, [None, None], name='label')
    learning_rate = tf.placeholder(tf.float32, name='learning_rate')

    return inputs, targets, learning_rate

#input_data, targets, lr = get_inputs()
#print((input_data.__doc__))

In [13]:
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_get_inputs(get_inputs)


Tests Passed

Build RNN Cell and Initialize

Stack one or more BasicLSTMCells in a MultiRNNCell.

  • The Rnn size should be set using rnn_size
  • Initalize Cell State using the MultiRNNCell's zero_state() function
    • Apply the name "initial_state" to the initial state using tf.identity()

Return the cell and initial state in the following tuple (Cell, InitialState)


In [14]:
lstm_layers = 1

def get_init_cell(batch_size, rnn_size):
    """
    Create an RNN Cell and initialize it.
    :param batch_size: Size of batches
    :param rnn_size: Size of RNNs
    :return: Tuple (cell, initial_state)
    """
    
    # Your basic LSTM cell
    lstm = tf.contrib.rnn.BasicLSTMCell(rnn_size)
    
    # Add dropout to the cell
    #drop = tf.contrib.rnn.DropoutWrapper(lstm, output_keep_prob=keep_prob)
    drop = lstm
    
    # Stack up multiple LSTM layers, for deep learning
    cell = tf.contrib.rnn.MultiRNNCell([drop] * lstm_layers)
    
    # Getting an initial state of all zeros
    initial_state = cell.zero_state(batch_size, tf.float32)
    
    initial_state = tf.identity(initial_state,'initial_state')
    
    return cell, initial_state

In [15]:
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_get_init_cell(get_init_cell)


Tests Passed

Word Embedding

Apply embedding to input_data using TensorFlow. Return the embedded sequence.


In [16]:
def get_embed(input_data, vocab_size, embed_dim):
    """
    Create embedding for <input_data>.
    :param input_data: TF placeholder for text input.
    :param vocab_size: Number of words in vocabulary.
    :param embed_dim: Number of embedding dimensions
    :return: Embedded input.
    """
    #print(input_data)
    #embedding = tf.Variable(tf.random_uniform((vocab_size, embed_dim), -1, 1),name='embedding_weights')
    #print(embedding)
    #embed = tf.nn.embedding_lookup(embedding, input_data)  
    #print(embed)

    return tf.contrib.layers.embed_sequence(input_data, vocab_size, embed_dim)

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_get_embed(get_embed)


Tests Passed

Build RNN

You created a RNN Cell in the get_init_cell() function. Time to use the cell to create a RNN.

Return the outputs and final_state state in the following tuple (Outputs, FinalState)


In [17]:
def build_rnn(cell, inputs):
    """
    Create a RNN using a RNN Cell
    :param cell: RNN Cell
    :param inputs: Input text data
    :return: Tuple (Outputs, Final State)
    """
    #print(inputs)
    
    outputs, final_state = tf.nn.dynamic_rnn(cell, inputs,
                                             dtype = tf.float32
                                             #initial_state=initial_state
                                            )
    
    final_state = tf.identity(final_state,'final_state')
    #print(final_state)
    #print(outputs)
    return outputs, final_state


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_build_rnn(build_rnn)


Tests Passed

Build the Neural Network

Apply the functions you implemented above to:

  • Apply embedding to input_data using your get_embed(input_data, vocab_size, embed_dim) function.
  • Build RNN using cell and your build_rnn(cell, inputs) function.
  • Apply a fully connected layer with a linear activation and vocab_size as the number of outputs.

Return the logits and final state in the following tuple (Logits, FinalState)


In [18]:
def build_nn(cell, rnn_size, input_data, vocab_size):
    """
    Build part of the neural network
    :param cell: RNN cell
    :param rnn_size: Size of rnns
    :param input_data: Input data
    :param vocab_size: Vocabulary size
    :return: Tuple (Logits, FinalState)
    """
    
    embed = get_embed(input_data, vocab_size, rnn_size)
    outputs, final_state = build_rnn(cell, embed)

    logits = tf.contrib.layers.fully_connected(
        outputs, 
        num_outputs=vocab_size,
        activation_fn=None,
        weights_initializer=tf.truncated_normal_initializer(stddev=0.01)
        )

    return logits, final_state


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_build_nn(build_nn)


Tests Passed

Batches

Implement get_batches to create batches of input and targets using int_text. The batches should be a Numpy array with the shape (number of batches, 2, batch size, sequence length). Each batch contains two elements:

  • The first element is a single batch of input with the shape [batch size, sequence length]
  • The second element is a single batch of targets with the shape [batch size, sequence length]

If you can't fill the last batch with enough data, drop the last batch.

For exmple, get_batches([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], 2, 3) would return a Numpy array of the following:

[
  # First Batch
  [
    # Batch of Input
    [[ 1  2  3], [ 7  8  9]],
    # Batch of targets
    [[ 2  3  4], [ 8  9 10]]
  ],

  # Second Batch
  [
    # Batch of Input
    [[ 4  5  6], [10 11 12]],
    # Batch of targets
    [[ 5  6  7], [11 12 13]]
  ]
]

In [19]:
import random

batch_distance_override = None

def get_batches(int_text, batch_size, seq_length, batch_distance = None):
    """
    Return batches of input and target
    :param int_text: Text with the words replaced by their ids
    :param batch_size: The size of batch
    :param seq_length: The length of sequence
    :return: Batches as a Numpy array
    """
    x = int_text[:-1]
    y = int_text[1:]
    
    #print('{} : {}'.format('seq_length',seq_length))
    single_batch_len = len(x)-seq_length
    #print('{} : {}'.format('single_batch_len',single_batch_len))
    single_batch = np.empty([2,single_batch_len,seq_length])
    
    single_batch_range = list(range(single_batch_len))
    
    random.shuffle(single_batch_range)
    #print(batch_range)
    
    ii2 = 0
    for ii in single_batch_range:
        single_batch [0,ii2] = x[ii:ii+seq_length]
        single_batch [1,ii2] = y[ii:ii+seq_length]
        ii2 += 1
    
    
    #################################################################################################################
    #
    #
    #  Question 1: why are the labels same length sequences of words not just the one word ?
    #
    #  Question 2: would it be better to set batch_distance to a smaller value ?
    #
    #
    #################################################################################################################

    if batch_distance == None:
        batch_distance = seq_length # would be 1 better?
        
    if batch_distance_override != None:
        batch_distance = batch_distance_override
        
    n_batches = single_batch_len//batch_size//batch_distance
    result = np.empty([n_batches,2,batch_size,seq_length])

    #print('{} : {}'.format('batch_size',batch_size))
    #print('{} : {}'.format('n_batches',n_batches))
    
    batch_range = (list(range(n_batches)))
    
    for ii in range(n_batches): #range(0, len(x), batch_size):
        iii = ii * batch_size * batch_distance
        result[ii] = single_batch[:,iii:iii+batch_size]

    return result


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_get_batches(get_batches)


Tests Passed

Neural Network Training

Hyperparameters

Tune the following parameters:

  • Set num_epochs to the number of epochs.
  • Set batch_size to the batch size.
  • Set rnn_size to the size of the RNNs.
  • Set seq_length to the length of sequence.
  • Set learning_rate to the learning rate.
  • Set show_every_n_batches to the number of batches the neural network should print progress.

In [20]:
###### Number of Epochs
num_epochs = 2**11
# Batch Size
batch_size = 127*2**2 # Primfaktoren von 69088: 2, 2, 2, 2, 2, 17, 127
# RNN Size
rnn_size = 256
# Sequence Length
seq_length = 10
# Learning Rate
learning_rate = 0.001
# Show stats for every n number of batches
show_every_n_batches = 130 * 5
# override batch_distance
batch_distance_override =  seq_length


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
save_dir = './save'

Build the Graph

Build the graph using the neural network you implemented.


In [21]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from tensorflow.contrib import seq2seq

train_graph = tf.Graph()
with train_graph.as_default():
    vocab_size = len(int_to_vocab)
    input_text, targets, lr = get_inputs()
    input_data_shape = tf.shape(input_text)
    cell, initial_state = get_init_cell(input_data_shape[0], rnn_size)
    logits, final_state = build_nn(cell, rnn_size, input_text, vocab_size)

    # Probabilities for generating words
    probs = tf.nn.softmax(logits, name='probs')

    # Loss function
    cost = seq2seq.sequence_loss(
        logits,
        targets,
        tf.ones([input_data_shape[0], input_data_shape[1]]))

    # Optimizer
    optimizer = tf.train.AdamOptimizer(lr)

    # Gradient Clipping
    gradients = optimizer.compute_gradients(cost)
    capped_gradients = [(tf.clip_by_value(grad, -1., 1.), var) for grad, var in gradients]
    train_op = optimizer.apply_gradients(capped_gradients)

Train

Train the neural network on the preprocessed data. If you have a hard time getting a good loss, check the forms to see if anyone is having the same problem.


In [22]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"" "

batches = get_batches(int_text, batch_size, seq_length)

with tf.Session(graph=train_graph) as sess:
    sess.run(tf.global_variables_initializer())

    for epoch_i in range(num_epochs):
        state = sess.run(initial_state, {input_text: batches[0][0]})

        for batch_i, (x, y) in enumerate(batches):
            feed = {
                input_text: x,
                targets: y,
                initial_state: state,
                lr: learning_rate}
            train_loss, state, _ = sess.run([cost, final_state, train_op], feed)

            # Show every <show_every_n_batches> batches
            if (epoch_i * len(batches) + batch_i) % show_every_n_batches == 0:
                print('Epoch {:>3} Batch {:>4}/{}   train_loss = {:.3f}'.format(
                    epoch_i,
                    batch_i,
                    len(batches),
                    train_loss))

    # Save Model
    saver = tf.train.Saver()
    saver.save(sess, save_dir)
    print('Model Trained and Saved')
        
""";

In [23]:
# added cyclic save and restore to interrupt training

# moved (rerandomised) batch generation in epoch loop
 
import matplotlib.pyplot as plt
import time

#batches = get_batches(int_text, batch_size, seq_length)

with tf.Session(graph=train_graph) as sess:
    
    try:
        assert() #
        saver.restore(sess, save_dir)
        print('Model Restored :)')
        with open('train_loss_history.p','rb') as file:
            train_loss_history = pickle.load(file)
        print('Loss_history Restored :)')
    except:
        train_loss_history = []
        sess.run(tf.global_variables_initializer())
        print('Initialised Model...')

    for epoch_i in range(num_epochs):
        
        # rerandomise batches
        batches = get_batches(int_text, batch_size, seq_length)

        state = sess.run(initial_state, {input_text: batches[0][0]})
    
        start = time.time()
        for batch_i, (x, y) in enumerate(batches):
            feed = {
                input_text: x,
                targets: y,
                initial_state: state,
                lr: learning_rate}
            train_loss, state, _ = sess.run([cost, final_state, train_op], feed)
            
            train_loss_history += [train_loss]

            # Show every <show_every_n_batches> batches
            if (epoch_i * len(batches) + batch_i) % show_every_n_batches == 0:
                print('Epoch {:>3} Batch {:>4}/{}   train_loss = {:.3f}'.format(
                    epoch_i,
                    batch_i,
                    len(batches),
                    train_loss))
                
            if(train_loss<0.1):
                break
        if(train_loss<0.1):
            break
                
                
    #print('computation time : {}'.format(time.time() - start))
    print('Epoch {:>3} Batch {:>4}/{}   train_loss = {:.3f}'.format(
        epoch_i,
        batch_i,
        len(batches),
        train_loss))
    # Save Model
    saver = tf.train.Saver()
    saver.save(sess, save_dir)
    pickle.dump((train_loss_history), open('train_loss_history.p', 'wb'))
    print('Model Trained and Saved')
    plt.plot(train_loss_history)
    plt.show()


Initialised Model...
Epoch   0 Batch    0/13   train_loss = 8.822
Epoch  50 Batch    0/13   train_loss = 4.641
Epoch 100 Batch    0/13   train_loss = 3.819
Epoch 150 Batch    0/13   train_loss = 3.201
Epoch 200 Batch    0/13   train_loss = 2.727
Epoch 250 Batch    0/13   train_loss = 2.361
Epoch 300 Batch    0/13   train_loss = 2.014
Epoch 350 Batch    0/13   train_loss = 1.870
Epoch 400 Batch    0/13   train_loss = 1.646
Epoch 450 Batch    0/13   train_loss = 1.500
Epoch 500 Batch    0/13   train_loss = 1.351
Epoch 550 Batch    0/13   train_loss = 1.222
Epoch 600 Batch    0/13   train_loss = 1.160
Epoch 650 Batch    0/13   train_loss = 1.066
Epoch 700 Batch    0/13   train_loss = 0.998
Epoch 750 Batch    0/13   train_loss = 0.932
Epoch 800 Batch    0/13   train_loss = 0.842
Epoch 850 Batch    0/13   train_loss = 0.865
Epoch 900 Batch    0/13   train_loss = 0.839
Epoch 950 Batch    0/13   train_loss = 0.809
Epoch 1000 Batch    0/13   train_loss = 0.788
Epoch 1050 Batch    0/13   train_loss = 0.767
Epoch 1100 Batch    0/13   train_loss = 0.759
Epoch 1150 Batch    0/13   train_loss = 0.686
Epoch 1200 Batch    0/13   train_loss = 0.712
Epoch 1250 Batch    0/13   train_loss = 0.689
Epoch 1300 Batch    0/13   train_loss = 0.687
Epoch 1350 Batch    0/13   train_loss = 0.667
Epoch 1400 Batch    0/13   train_loss = 0.642
Epoch 1450 Batch    0/13   train_loss = 0.633
Epoch 1500 Batch    0/13   train_loss = 0.665
Epoch 1550 Batch    0/13   train_loss = 0.629
Epoch 1600 Batch    0/13   train_loss = 0.645
Epoch 1650 Batch    0/13   train_loss = 0.619
Epoch 1700 Batch    0/13   train_loss = 0.635
Epoch 1750 Batch    0/13   train_loss = 0.623
Epoch 1800 Batch    0/13   train_loss = 0.634
Epoch 1850 Batch    0/13   train_loss = 0.626
Epoch 1900 Batch    0/13   train_loss = 0.625
Epoch 1950 Batch    0/13   train_loss = 0.596
Epoch 2000 Batch    0/13   train_loss = 0.596
Epoch 2047 Batch   12/13   train_loss = 0.612
Model Trained and Saved

Save Parameters

Save seq_length and save_dir for generating a new TV script.


In [24]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
# Save parameters for checkpoint
helper.save_params((seq_length, save_dir))

Checkpoint


In [25]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import tensorflow as tf
import numpy as np
import helper
import problem_unittests as tests

_, vocab_to_int, int_to_vocab, token_dict = helper.load_preprocess()
seq_length, load_dir = helper.load_params()

Implement Generate Functions

Get Tensors

Get tensors from loaded_graph using the function get_tensor_by_name(). Get the tensors using the following names:

  • "input:0"
  • "initial_state:0"
  • "final_state:0"
  • "probs:0"

Return the tensors in the following tuple (InputTensor, InitialStateTensor, FinalStateTensor, ProbsTensor)


In [26]:
def get_tensors(loaded_graph):
    """
    Get input, initial state, final state, and probabilities tensor from <loaded_graph>
    :param loaded_graph: TensorFlow graph loaded from file
    :return: Tuple (InputTensor, InitialStateTensor, FinalStateTensor, ProbsTensor)
    """
    InputTensor        = loaded_graph.get_tensor_by_name("input:0")
    InitialStateTensor = loaded_graph.get_tensor_by_name("initial_state:0")
    FinalStateTensor   = loaded_graph.get_tensor_by_name("final_state:0")
    ProbsTensor        = loaded_graph.get_tensor_by_name("probs:0")
    return InputTensor, InitialStateTensor, FinalStateTensor, ProbsTensor


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_get_tensors(get_tensors)


Tests Passed

Choose Word

Implement the pick_word() function to select the next word using probabilities.


In [27]:
def pick_word(probabilities, int_to_vocab):
    """
    Pick the next word in the generated text
    :param probabilities: Probabilites of the next word
    :param int_to_vocab: Dictionary of word ids as the keys and words as the values
    :return: String of the predicted word
    """
    
    p2 = sorted(list(zip(list(probabilities),range(len(probabilities)))))

    # ideas : randomise by threshold, memorise last picks to control repetition, ad theme dict, alliteration

    return int_to_vocab[p2[-1][-1]]


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_pick_word(pick_word)


Tests Passed

Generate TV Script

This will generate the TV script for you. Set gen_length to the length of TV script you want to generate.


In [28]:
gen_length = 1000
# homer_simpson, moe_szyslak, or Barney_Gumble
prime_word = 'moe_szyslak'

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
loaded_graph = tf.Graph()
with tf.Session(graph=loaded_graph) as sess:
    # Load saved model
    loader = tf.train.import_meta_graph(load_dir + '.meta')
    loader.restore(sess, load_dir)

    # Get Tensors from loaded model
    input_text, initial_state, final_state, probs = get_tensors(loaded_graph)

    # Sentences generation setup
    gen_sentences = [prime_word + ':']
    prev_state = sess.run(initial_state, {input_text: np.array([[1]])})

    # Generate sentences
    for n in range(gen_length):
        # Dynamic Input
        dyn_input = [[vocab_to_int[word] for word in gen_sentences[-seq_length:]]]
        dyn_seq_length = len(dyn_input[0])

        # Get Prediction
        probabilities, prev_state = sess.run(
            [probs, final_state],
            {input_text: dyn_input, initial_state: prev_state})
        
        pred_word = pick_word(probabilities[dyn_seq_length-1], int_to_vocab)

        gen_sentences.append(pred_word)
    
    # Remove tokens
    tv_script = ' '.join(gen_sentences)
    for key, token in token_dict.items():
        ending = ' ' if key in ['\n', '(', '"'] else ''
        tv_script = tv_script.replace(' ' + token.lower(), key)
    tv_script = tv_script.replace('\n ', '\n')
    tv_script = tv_script.replace('( ', '(')
        
    print(tv_script)


moe_szyslak:(into phone) moe's tavern. moe speaking.
bart_simpson:(into phone) uh yes, i'm looking for a mrs. o'problem. first name... bee.
moe_szyslak:(into phone) ah, yeah. just a minute, i'll check.(calling out) uh, bee o'problem. bee o'problem. c'mon, guys. do i have an o'problem here?
barney_gumble: you sure do!
moe_szyslak:(realizing) awwww.(into phone) it's you, isn't it?
moe_szyslak: listen you, when i get a hold of you, i'm gonna use your head for a bucket and paint my house with your brains!
bart_simpson: excuse me, i'm looking for--
moe_szyslak: wait a minute. i know that voice.
moe_szyslak: if it isn't little bart simpson! i haven't seen you in years.
bart_simpson: that's right. that's my pop!
moe_szyslak: ah, little bart... we hear all about your monkeyshines.
moe_szyslak:(conspiratorial) bet you get into all kinds of trouble he don't even know about. am i right? huh? am i right?
bart_simpson:(can't resist) yeah, well, i make some crank phone calls.
moe_szyslak:(musses bart's hair)(laughs) that's great! hey, would you sing that old song you used to sing for me?
bart_simpson:(a little embarrassed) moe, for you... anything?
bart_simpson:(singing) every teddy bear who's been good is sure of a treat today/ there's lots of marvelous things to eat, and wonderful games to play/ beneath the trees, where nobody sees/ they'll hide and seek as long as they please/ today's the day the teddy bears have their picnic!
moe_szyslak: he's a pip, this one is!
c. _montgomery_burns: ah, the mirthless laugh of the damned. hold your nose, smithers, we're going in!
c. _montgomery_burns:(to smithers) watch me blend in.(to moe) barkeep, some cheap domestic beer for me and my" buddy" here.
homer_simpson: i'm not your buddy, you greedy old reptile!
c. _montgomery_burns: smithers, who is this saucy fellow?
waylon_smithers: homer simpson, sir. sector sieben-gruben-- i mean, sector 7g. recently terminated.
homer_simpson: that's right. i lost my job so that you could have another 100 million dollars.
homer_simpson:(pointed) let me ask you something. does your money cheer you up when you're feeling blue?
c. _montgomery_burns: yes.
homer_simpson: okay, bad example. so let me ask you this, does your money ever hug you when you come home at night?
c. _montgomery_burns:(shaken) why, no.
homer_simpson: and does it ever say," i love you?"
c. _montgomery_burns:(shaken) no, it doesn't.
homer_simpson:(sing-song) nobody loves you. nobody loves you. you're old and you're ugly. nobody loves you. yea, yea, yea yea!
homer_simpson: nobody loves you...
c. _montgomery_burns: good heavens, smithers! they're not afraid of me anymore!
bart_simpson: hey mr. burns, did you get that letter i sent?
c. _montgomery_burns: letter? i don't recall any letter...
bart_simpson: that's because i forgot to stamp it.
moe_szyslak:(laughing) ah, that kid slays me.
c. _montgomery_burns: that was no accident. let's get out of here.
homer_simpson:(singing) na na na na / na na na na /
barflies: hey hey hey / goodbye-- na na na na / na na na na /
barflies: hey hey hey / goodbye-- na na na na / na na na na /
barflies: hey hey hey / goodbye-- na na na na / na na na na /
barflies: hey hey hey / goodbye-- na na na na / na na na na /
barflies: hey hey hey / goodbye-- na na na na / na na na na /
barflies: hey hey hey / goodbye-- na na na na / na na na na /
barflies: hey hey hey / goodbye-- na na na na / na na na na /
barflies: hey hey hey / goodbye-- na na na na / na na na na /
barflies: hey hey hey / goodbye-- na na na na / na na na na /
barflies: hey hey hey / goodbye-- na na na na / na na na na /
barflies: hey hey hey / goodbye-- na na na na / na na na na /
barflies: hey hey hey / goodbye-- na na na na / na na na na /
barflies: hey hey hey / goodbye-- na na na na / na na na na /
barflies: hey hey hey / goodbye-- na na na na / na na na na /
barflies: hey hey hey / goodbye-- na na na na / na na na na /
barflies: hey hey hey / goodbye-- na na na na / na na na na /
barflies: hey hey hey / goodbye-- na na na na / na na na na /
barflies: hey hey hey / goodbye

Further optimisation for The TV Script

  1. Add more Data, The used Dataset is a subset of another dataset. It wasn't trained on all the data, because that would take too much time and ressources.
  2. Add LSTM Layers, seq_len & Epochs (more should be at least somehow better, but will increase the computationally effort)
  3. Finetune batch_size,rnn_size & learningrate

Feel free to fork and/or contrbute!