# Notebook from Pogayo/ADH-EN_MT_Dataset
Path: Spam_Classifier.ipynb

_____no_output_____

import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from google.colab import drive_____no_output_____drive.mount('./drive')Mounted at ./drive
!cp "/content/drive/MyDrive/Year 4/Machine Learning/spam_or_not_spam.csv.zip" spam_or_not_spam.csv.zip
!unzip -q spam_or_not_spam.csv.zip_____no_output_____df=pd.read_csv("spam_or_not_spam.csv")_____no_output_____df.head()_____no_output_____df.describe()_____no_output_____df[df.label==1]_____no_output_____df.iloc[0,0]_____no_output_____

**If an email is spam - 1 if no, 0**_____no_output_____##Splitting the dataset_____no_output_____

import matplotlib.pyplot as plt
import seaborn as sns_____no_output_____df.groupby('label').describe().T_____no_output_____ham_size=df.groupby('label').describe().T.iloc[0,0]
spam_size=df.groupby('label').describe().T.iloc[0,1]
print(ham_size, spam_size)2500 499
# Visualizing imbalance data here
plt.figure(figsize=(8,6))
sns.countplot(df.label)/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  FutureWarning
(spam_size/len(df))*100 # # Percentage of spam messages_____no_output_____

This is an imbalanced dataset, so we should use a split strategy that takes this into account._____no_output_____

from keras.utils import to_categorical
X=df["email"]
y_encoded=to_categorical(df["label"]) #encoding the data
y=df["label"]_____no_output__________no_output_____X=X.apply(str) #making sure we are only dealing with strings_____no_output_____X_train, X_test, y_train, y_test=train_test_split(X, y,test_size=0.2, random_state=23, stratify=y) #using stratify to make the split proportional to the different labels_____no_output_____type(X)_____no_output_____

##Creating and saving the vocabulary_____no_output_____

from keras.preprocessing.text import Tokenizer

# create the tokenizer
t = Tokenizer()
# fit the tokenizer on the documents
t.fit_on_texts(X.values)

print(t.word_counts)OrderedDict([('date', 874), ('wed', 144), ('number', 33888), ('aug', 213), ('from', 3454), ('chris', 153), ('garrigues', 57), ('cwg', 22), ('dated', 29), ('numberfanumberd', 2), ('deepeddy', 30), ('com', 429), ('message', 854), ('id', 89), ('tmda', 51), ('vircio', 44), ('i', 9009), ('can', 2417), ('t', 3309), ('reproduce', 12), ('this', 5324), ('error', 278), ('for', 6760), ('me', 1107), ('it', 7059), ('is', 8007), ('very', 530), ('repeatable', 1), ('like', 1248), ('every', 412), ('time', 1294), ('without', 379), ('fail', 37), ('the', 28722), ('debug', 35), ('log', 216), ('of', 14264), ('pick', 97), ('happening', 16), ('exec', 15), ('inbox', 51), ('list', 2177), ('lbrace', 8), ('subject', 487), ('ftp', 25), ('rbrace', 8), ('sequence', 84), ('mercury', 7), ('ftoc', 59), ('pickmsgs', 1), ('hit', 142), ('marking', 6), ('hits', 32), ('tkerror', 1), ('syntax', 20), ('in', 9490), ('expression', 24), ('int', 52), ('note', 125), ('if', 2625), ('run', 414), ('command', 94), ('by', 2672), ('hand', 127), ('delta', 19), ('that', 7515), ('s', 4915), ('where', 599), ('comes', 102), ('obviously', 44), ('version', 287), ('nmh', 31), ('m', 997), ('using', 576), ('compiled', 43), ('on', 5033), ('url', 8686), ('at', 2686), ('sun', 116), ('mar', 1), ('ict', 1), ('and', 14390), ('relevant', 46), ('part', 284), ('my', 2045), ('mh', 69), ('profile', 18), ('mhparam', 1), ('seq', 31), ('sel', 27), ('since', 404), ('works', 214), ('actually', 296), ('both', 289), ('them', 926), ('one', 1675), ('explicit', 9), ('line', 526), ('search', 154), ('popup', 8), ('do', 1655), ('get', 1598), ('created', 68), ('kre', 33), ('ps', 32), ('still', 473), ('code', 325), ('form', 260), ('a', 12982), ('day', 421), ('ago', 174), ('haven', 94), ('been', 1003), ('able', 186), ('to', 18409), ('reach', 83), ('cvs', 122), ('repository', 46), ('today', 391), ('local', 204), ('routing', 18), ('issue', 175), ('think', 699), ('exmh', 620), ('workers', 140), ('mailing', 871), ('martin', 30), ('posted', 121), ('tassos', 1), ('papadopoulos', 1), ('greek', 2), ('sculptor', 3), ('behind', 72), ('plan', 129), ('judged', 5), ('limestone', 5), ('mount', 10), ('kerdylio', 1), ('miles', 42), ('east', 41), ('salonika', 1), ('not', 3403), ('far', 212), ('athos', 1), ('monastic', 1), ('community', 105), ('was', 2357), ('ideal', 12), ('patriotic', 1), ('sculpture', 3), ('as', 3352), ('well', 576), ('alexander', 2), ('granite', 2), ('features', 75), ('ft', 12), ('high', 296), ('wide', 44), ('museum', 10), ('restored', 9), ('amphitheatre', 1), ('car', 89), ('park', 43), ('admiring', 1), ('crowds', 2), ('are', 3435), ('planned', 17), ('so', 1776), ('mountain', 26), ('or', 3153), ('ll', 559), ('weather', 32), ('pretty', 139), ('fast', 128), ('yahoo', 276), ('groups', 292), ('sponsor', 103), ('dvds', 83), ('free', 1180), ('p', 230), ('join', 127), ('now', 1152), ('unsubscribe', 378), ('group', 472), ('send', 541), ('an', 2219), ('email', 1175), ('forteana', 152), ('your', 3062), ('use', 1258), ('man', 175), ('threatens', 7), ('explosion', 5), ('moscow', 9), ('thursday', 59), ('august', 140), ('pm', 221), ('ap', 17), ('security', 337), ('officers', 13), ('seized', 13), ('unidentified', 2), ('who', 1190), ('said', 803), ('he', 1193), ('armed', 27), ('with', 4178), ('explosives', 2), ('threatened', 11), ('blow', 14), ('up', 1258), ('his', 795), ('truck', 21), ('front', 48), ('russia', 42), ('federal', 129), ('services', 241), ('headquarters', 14), ('ntv', 2), ('television', 39), ('reported', 80), ('automatic', 26), ('rifle', 3), ('carrying', 18), ('then', 888), ('got', 345), ('out', 1504), ('taken', 99), ('into', 773), ('custody', 8), ('no', 1514), ('other', 1076), ('details', 88), ('were', 723), ('immediately', 109), ('available', 236), ('had', 803), ('demanded', 6), ('talks', 23), ('government', 480), ('officials', 57), ('interfax', 2), ('itar', 2), ('tass', 2), ('news', 283), ('agencies', 44), ('ekho', 1), ('moskvy', 1), ('radio', 60), ('wanted', 117), ('talk', 383), ('russian', 25), ('president', 259), ('vladimir', 2), ('putin', 1), ('police', 84), ('forces', 96), ('rushed', 3), ('service', 303), ('building', 115), ('within', 318), ('blocks', 8), ('kremlin', 1), ('red', 222), ('square', 28), ('bolshoi', 1), ('ballet', 2), ('surrounded', 3), ('claimed', 23), ('have', 3468), ('half', 148), ('tons', 14), ('negotiations', 11), ('continued', 18), ('about', 1485), ('hours', 164), ('outside', 67), ('citing', 4), ('witnesses', 12), ('later', 142), ('drove', 13), ('away', 174), ('under', 276), ('escort', 3), ('street', 109), ('near', 68), ('olympic', 2), ('penta', 1), ('hotel', 33), ('authorities', 14), ('held', 65), ('further', 172), ('him', 207), ('press', 101), ('move', 166), ('appeared', 18), ('be', 3704), ('attempt', 51), ('more', 1655), ('secure', 109), ('location', 37), ('klez', 9), ('virus', 43), ('won', 221), ('die', 35), ('already', 205), ('most', 670), ('prolific', 2), ('ever', 245), ('continues', 40), ('wreak', 3), ('havoc', 7), ('andrew', 21), ('brandt', 1), ('september', 298), ('pc', 79), ('world', 807), ('magazine', 28), ('worm', 3), ('approaching', 9), ('its', 739), ('seventh', 13), ('month', 169), ('wriggling', 1), ('across', 116), ('web', 420), ('making', 203), ('persistent', 6), ('viruses', 58), ('experts', 38), ('warn', 4), ('may', 611), ('harbinger', 1), ('new', 1200), ('combination', 15), ('pernicious', 2), ('approaches', 13), ('go', 531), ('antivirus', 2), ('software', 440), ('makers', 22), ('symantec', 6), ('mcafee', 1), ('report', 406), ('than', 943), ('infections', 2), ('daily', 127), ('sign', 55), ('letup', 1), ('british', 74), ('firm', 53), ('messagelabs', 3), ('estimates', 15), ('e', 896), ('mail', 1041), ('messages', 403), ('holds', 23), ('variation', 3), ('says', 229), ('has', 1722), ('surpassed', 2), ('last', 380), ('summer', 42), ('sircam', 2), ('some', 1123), ('newer', 14), ('variants', 1), ('aren', 81), ('merely', 19), ('nuisances', 1), ('they', 2022), ('carry', 36), ('corrupt', 12), ('data', 450), ('irregulars', 6), ('adding', 62), ('cream', 11), ('spaghetti', 2), ('carbonara', 4), ('which', 1320), ('same', 573), ('effect', 65), ('pasta', 5), ('pizza', 2), ('deep', 32), ('pie', 13), ('just', 1509), ('jump', 36), ('here', 1026), ('favourites', 2), ('make', 879), ('ask', 104), ('what', 1413), ('hell', 60), ('you', 7289), ('supposed', 70), ('instead', 226), ('ve', 694), ('never', 330), ('seen', 185), ('recipe', 17), ('hasn', 41), ('used', 402), ('personally', 47), ('low', 130), ('fat', 27), ('creme', 2), ('fraiche', 2), ('because', 794), ('quite', 152), ('nicely', 6), ('but', 2564), ('only', 1252), ('supposedly', 13), ('authentic', 3), ('identical', 23), ('mine', 35), ('eggs', 36), ('lots', 93), ('fresh', 25), ('parmesan', 1), ('except', 102), ('stew', 10), ('stewart', 8), ('smith', 19), ('scottish', 8), ('microelectronics', 5), ('centre', 16), ('university', 105), ('edinburgh', 12), ('isn', 205), ('basically', 38), ('mixture', 3), ('beaten', 8), ('egg', 13), ('bacon', 6), ('pancetta', 2), ('really', 427), ('mix', 22), ('raw', 33), ('cooked', 4), ('heat', 12), ('cooks', 10), ('understanding', 36), ('scotsman', 3), ('playboy', 3), ('wants', 97), ('bang', 17), ('ageing', 3), ('berlin', 10), ('come', 264), ('unusual', 25), ('offer', 231), ('lure', 2), ('women', 170), ('bed', 21), ('promising', 9), ('woman', 70), ('sleeps', 3), ('inheritance', 6), ('rolf', 1), ('eden', 3), ('disco', 1), ('owner', 34), ('famous', 26), ('countless', 8), ('sex', 120), ('partners', 114), ('could', 718), ('imagine', 54), ('better', 366), ('way', 728), ('arms', 23), ('attractive', 17), ('young', 83), ('preferably', 6), ('put', 276), ('all', 2212), ('will', 2205), ('testament', 2), ('gets', 131), ('money', 722), ('mr', 313), ('told', 129), ('bild', 2), ('newspaper', 27), ('want', 641), ('pass', 35), ('beautiful', 39), ('moment', 73), ('life', 338), ('first', 719), ('lot', 244), ('fun', 73), ('wild', 21), ('final', 33), ('orgasm', 4), ('end', 322), ('heart', 38), ('attack', 72), ('gone', 50), ('selling', 84), ('nightclub', 1), ('year', 403), ('applications', 93), ('should', 698), ('sent', 362), ('quickly', 63), ('age', 84), ('soon', 106), ('adamson', 4), ('wrote', 921), ('re', 762), ('probably', 285), ('right', 508), ('added', 129), ('guess', 102), ('try', 324), ('looking', 252), ('internet', 473), ('found', 353), ('possibly', 38), ('scariest', 1), ('people', 1315), ('us', 798), ('congressman', 2), ('worst', 61), ('non', 224), ('smiles', 6), ('apologies', 29), ('any', 1182), ('maine', 14), ('residents', 26), ('voted', 1), ('again', 340), ('once', 327), ('pic', 2), ('thu', 129), ('meaningful', 11), ('sentences', 5), ('tracey', 1), ('lawson', 2), ('look', 318), ('dangerous', 32), ('inmates', 2), ('prison', 21), ('history', 99), ('judge', 26), ('described', 38), ('charles', 11), ('bronson', 12), ('chance', 62), ('serial', 41), ('hostage', 3), ('taker', 1), ('movie', 56), ('star', 15), ('written', 79), ('health', 94), ('fitness', 16), ('guide', 150), ('shares', 29), ('secrets', 35), ('legendary', 7), ('muscle', 12), ('power', 175), ('solitary', 4), ('title', 54), ('bears', 5), ('fact', 189), ('spent', 61), ('years', 524), ('confinement', 1), ('explains', 19), ('how', 943), ('turned', 58), ('himself', 44), ('lean', 7), ('mean', 143), ('machine', 112), ('while', 382), ('living', 107), ('space', 105), ('feet', 57), ('eight', 15), ('diet', 7), ('scrubs', 1), ('grub', 7), ('virtually', 25), ('cost', 140), ('book', 128), ('aimed', 9), ('those', 572), ('fabulously', 1), ('fit', 30), ('spending', 46), ('fortune', 47), ('gym', 1), ('memberships', 1), ('protein', 2), ('supplements', 3), ('designer', 16), ('trainers', 1), ('starts', 36), ('fierce', 3), ('expensive', 32), ('myths', 1), ('churned', 1), ('exercise', 24), ('industry', 165), ('mag', 2), ('start', 267), ('laugh', 20), ('wipe', 11), ('arse', 2), ('opening', 24), ('paragraph', 17), ('penned', 2), ('joke', 26), ('big', 202), ('con', 20), ('call', 252), ('criminal', 33), ('help', 457), ('feeling', 36), ('point', 280), ('bars', 10), ('having', 194), ('published', 63), ('birdman', 1), ('opens', 12), ('mind', 145), ('drawings', 3), ('poems', 1), ('prisoner', 1), ('discover', 61), ('creative', 37), ('residing', 5), ('her', 294), ('majesty', 25), ('pleasure', 11), ('jimmy', 3), ('boyle', 4), ('scots', 3), ('novelist', 7), ('discovered', 51), ('artistic', 3), ('talents', 2), ('when', 1097), ('barlinnie', 2), ('special', 200), ('unit', 24), ('their', 1270), ('violent', 14), ('pasts', 2), ('teaching', 11), ('express', 16), ('emotions', 5), ('artistically', 1), ('sentenced', 5), ('murder', 25), ('babs', 1), ('rooney', 1), ('released', 53), ('moved', 66), ('become', 159), ('respected', 4), ('artist', 18), ('novel', 8), ('hero', 3), ('underworld', 2), ('autobiography', 3), ('sense', 88), ('freedom', 152), ('made', 403), ('award', 16), ('winning', 22), ('film', 44), ('hugh', 4), ('collins', 3), ('jailed', 7), ('william', 25), ('mooney', 2), ('glasgow', 2), ('stabbed', 3), ('three', 191), ('earning', 10), ('extra', 70), ('seven', 49), ('sentence', 11), ('after', 585), ('being', 483), ('transferred', 21), ('attended', 12), ('learned', 31), ('sculpt', 1), ('developed', 71), ('interest', 143), ('art', 39), ('murderer', 2), ('frank', 16), ('account', 159), ('culture', 39), ('numbers', 195), ('received', 281), ('critical', 55), ('praise', 3), ('lord', 12), ('archer', 3), ('doesn', 355), ('seem', 161), ('trouble', 59), ('continuing', 23), ('write', 152), ('books', 56), ('millions', 71), ('jail', 9), ('recently', 111), ('signed', 68), ('deal', 153), ('macmillan', 1), ('publishers', 15), ('worth', 118), ('million', 305), ('doubt', 51), ('scribbling', 1), ('we', 2540), ('speak', 50), ('why', 395), ('men', 131), ('destructive', 28), ('towards', 41), ('society', 105), ('stuck', 26), ('inside', 71), ('steve', 57), ('richards', 2), ('publisher', 17), ('many', 567), ('figures', 28), ('believes', 34), ('roots', 14), ('phenomenon', 5), ('pragmatic', 3), ('profound', 13), ('sometimes', 75), ('criminals', 6), ('known', 129), ('stable', 31), ('environment', 57), ('focus', 57), ('skills', 27), ('also', 914), ('education', 67), ('early', 102), ('hard', 172), ('anyone', 327), ('offered', 29), ('explore', 13), ('however', 229), ('reasons', 80), ('deeper', 9), ('cold', 41), ('light', 56), ('examine', 5), ('essence', 13), ('themselves', 81), ('am', 517), ('remembered', 3), ('violence', 44), ('contribute', 10), ('something', 445), ('good', 547), ('born', 25), ('michael', 78), ('gordon', 37), ('peterson', 7), ('changed', 116), ('name', 467), ('hollywood', 13), ('death', 87), ('wish', 229), ('films', 10), ('mainly', 17), ('things', 311), ('bad', 155), ('originally', 25), ('robbery', 5), ('series', 41), ('original', 287), ('term', 114), ('over', 756), ('result', 73), ('attacking', 5), ('convicted', 5), ('holding', 22), ('teacher', 9), ('nearly', 42), ('two', 463), ('days', 220), ('during', 169), ('siege', 2), ('standing', 14), ('five', 105), ('ten', 48), ('inches', 16), ('tall', 28), ('weighing', 3), ('numberlbs', 9), ('renowned', 1), ('strength', 39), ('bent', 9), ('metal', 9), ('cell', 150), ('doors', 6), ('bare', 6), ('hands', 56), ('does', 552), ('yes', 290), ('ups', 9), ('puts', 18), ('times', 263), ('four', 82), ('seconds', 53), ('push', 13), ('judging', 4), ('our', 1547), ('current', 228), ('obsession', 1), ('might', 333), ('see', 634), ('face', 102), ('sitting', 34), ('coffee', 24), ('table', 55), ('land', 57), ('give', 322), ('dream', 42), ('body', 107), ('called', 200), ('gurus', 5), ('promise', 51), ('motivate', 6), ('words', 124), ('powerfully', 1), ('fists', 2), ('crap', 29), ('drinks', 7), ('pills', 7), ('diets', 2), ('load', 130), ('bollocks', 1), ('multi', 39), ('pound', 13), ('racket', 1), ('writes', 182), ('refreshingly', 1), ('honest', 21), ('style', 56), ('lazy', 8), ('bastards', 6), ('choice', 93), ('sick', 14), ('hearing', 36), ('reading', 117), ('excuses', 6), ('stuff', 239), ('shit', 16), ('logical', 17), ('motivational', 1), ('mantras', 1), ('kick', 19), ('er', 13), ('backside', 2), ('needed', 136), ('mirage', 1), ('publishing', 27), ('bookstores', 3), ('october', 108), ('trying', 225), ('research', 116), ('via', 169), ('sa', 127), ('mirrors', 21), ('engines', 49), ('canned', 1), ('script', 89), ('exists', 52), ('giving', 67), ('clients', 74), ('access', 252), ('user', 288), ('prefs', 19), ('options', 55), ('based', 311), ('cgi', 9), ('interface', 53), ('numerous', 15), ('isps', 12), ('provide', 183), ('feature', 114), ('find', 465), ('nothing', 215), ('configuration', 18), ('uses', 65), ('amavis', 4), ('postfix', 8), ('clamav', 1), ('filtering', 38), ('procmail', 113), ('spamassassin', 754), ('spam', 666), ('would', 1254), ('prefer', 41), ('myself', 62), ('appreciate', 23), ('suggestions', 33), ('sponsored', 277), ('osdn', 117), ('tired', 136), ('old', 409), ('phone', 377), ('hello', 56), ('discussed', 14), ('article', 121), ('approach', 51), ('thank', 102), ('there', 1636), ('rules', 121), ('accomplish', 13), ('thomas', 39), ('alva', 1), ('edison', 1), ('devel', 150), ('great', 309), ('minds', 18), ('alike', 6), ('even', 685), ('withput', 1), ('eval', 71), ('useful', 88), ('allow', 153), ('respond', 35), ('spammer', 42), ('tricks', 11), ('theo', 22), ('van', 42), ('dinter', 10), ('numberam', 43), ('marc', 5), ('perkel', 3), ('though', 247), ('idea', 214), ('live', 177), ('updates', 57), ('release', 231), ('cron', 12), ('job', 152), ('week', 213), ('default', 79), ('rule', 92), ('set', 373), ('react', 5), ('faster', 51), ('suggested', 22), ('few', 406), ('months', 175), ('don', 1089), ('remember', 159), ('came', 145), ('require', 73), ('changes', 95), ('mon', 130), ('numberpm', 131), ('john', 141), ('looney', 9), ('mentioned', 47), ('likely', 90), ('boot', 29), ('cobalt', 1), ('passing', 25), ('root', 216), ('dev', 67), ('hdanumber', 3), ('kernel', 254), ('mdnumber', 19), ('solve', 39), ('reason', 155), ('booting', 6), ('box', 156), ('wasn', 98), ('barf', 1), ('init', 26), ('turns', 39), ('populating', 1), ('tar', 15), ('seems', 254), ('issues', 98), ('crosslinked', 1), ('files', 334), ('instance', 40), ('link', 362), ('hda', 2), ('failing', 14), ('did', 381), ('friend', 87), ('present', 62), ('spotted', 14), ('errors', 47), ('queried', 3), ('noticed', 57), ('linked', 22), ('didn', 262), ('exist', 84), ('rootfs', 1), ('duplicated', 3), ('filesystems', 3), ('dump', 27), ('worked', 105), ('fine', 104), ('tell', 184), ('lilo', 3), ('everything', 150), ('woohoo', 6), ('kate', 6), ('irish', 192), ('linux', 558), ('users', 719), ('ilug', 189), ('un', 242), ('subscription', 200), ('information', 707), ('maintainer', 173), ('listmaster', 168), ('ouch', 28), ('robert', 109), ('elz', 21), ('munnari', 10), ('oz', 33), ('au', 45), ('lengthen', 1), ('nice', 147), ('columns', 8), ('fits', 14), ('display', 82), ('detache', 1), ('d', 744), ('folder', 239), ('column', 26), ('main', 100), ('window', 126), ('takes', 85), ('full', 208), ('screen', 48), ('top', 193), ('bottom', 53), ('less', 271), ('width', 45), ('etc', 289), ('thought', 193), ('order', 363), ('approximation', 3), ('add', 207), ('pack', 22), ('side', 84), ('left', 125), ('each', 406), ('different', 320), ('funny', 43), ('done', 264), ('going', 338), ('leave', 79), ('cosmetic', 3), ('others', 183), ('update', 108), ('documentation', 36), ('todo', 5), ('file', 483), ('leaving', 32), ('vacation', 16), ('functionality', 14), ('much', 581), ('own', 461), ('work', 694), ('bug', 118), ('fixes', 15), ('before', 500), ('hopefully', 25), ('afterwards', 8), ('congress', 89), ('suite', 77), ('austin', 35), ('tx', 38), ('war', 251), ('iii', 57), ('wrong', 170), ('doers', 61), ('vs', 66), ('evil', 63), ('hurting', 7), ('democracy', 42), ('owen', 58), ('level', 136), ('political', 187), ('playing', 70), ('field', 57), ('mike', 52), ('mccurry', 2), ('larry', 17), ('purpuro', 2), ('country', 246), ('experience', 176), ('recurring', 5), ('american', 252), ('shopping', 21), ('malls', 1), ('factory', 4), ('floors', 2), ('church', 31), ('socials', 1), ('stoops', 1), ('approached', 16), ('individuals', 87), ('represent', 19), ('public', 239), ('office', 157), ('chances', 13), ('know', 654), ('walk', 34), ('handshake', 9), ('flier', 1), ('votes', 7), ('technology', 298), ('affecting', 3), ('area', 125), ('communication', 54), ('begun', 18), ('affect', 7), ('candidates', 25), ('communicate', 17), ('voters', 18), ('gop', 1), ('gubernatorial', 4), ('primary', 47), ('california', 64), ('secretary', 28), ('state', 369), ('bill', 134), ('jones', 18), ('faced', 11), ('funded', 5), ('acquired', 24), ('addresses', 173), ('potential', 100), ('unsolicited', 57), ('asking', 48), ('support', 265), ('chosen', 25), ('traditional', 36), ('methods', 37), ('contacting', 6), ('such', 495), ('direct', 103), ('spots', 15), ('tv', 56), ('ads', 46), ('cents', 25), ('per', 273), ('another', 351), ('medium', 20), ('equally', 10), ('defined', 42), ('opted', 32), ('receive', 324), ('campaign', 48), 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('redeemable', 1), ('southafrican', 1), ('amouth', 2), ('ireceive', 1), ('unforseen', 1), ('transfering', 1), ('manuelokonumber', 1), ('clearifications', 1), ('desmond', 3), ('gemstones', 1), ('demurrage', 1), ('swaziland', 1), ('lunatics', 1), ('prohibit', 1), ('eldest', 1), ('saddled', 1), ('entrusted', 1), ('nominating', 1), ('frantically', 1), ('dianne', 1), ('plush', 1), ('novice', 1), ('resellers', 1), ('子', 5), ('件', 6), ('number世', 1), ('o最有效的宣', 1), ('鞣绞剑', 1), ('您想', 1), ('地number', 1), ('f人同一', 1), ('r刻知道您的', 1), ('v告', 4), ('幔磕', 1), ('想方便快捷的宣', 1), ('髂', 1), ('的企', 1), ('幔', 3), ('您想在商', 1), ('中步步', 1), ('紫', 1), ('您想一次', 1), ('碛', 2), ('在商', 1), ('i客', 1), ('越', 1), ('碓蕉嗟恼', 1), ('查表明', 1), ('w路直', 1), ('成', 1), ('未', 1), ('n方式的主流', 1), ('而e', 1), ('⑹蔷w上', 1), ('n最常用也是最', 1), ('用的工具', 1), ('其特', 1), ('r效高', 1), ('r格低', 1), ('emarketer估', 1), ('有number', 1), ('的中等', 1), ('模公司', 1), ('常', 1), ('用', 1), ('m行', 1), ('n活', 1), ('印', 1), ('部分', 1), ('v告媒', 1), ('w平均回', 1), ('率比', 1), ('普通横幅广告', 1), ('普通信件', 1), ('一', 1), ('件出售', 1), ('i全球', 1), ('件地址', 3), ('每number', 4), ('numberhk', 11), ('i全部number', 1), ('i香港', 1), ('i香港全部number', 1), ('赤', 1), ('橙', 1), ('部number', 1), ('l送', 2), ('件代理', 1), ('f起', 1), ('蚀', 1), ('率number', 1), ('以上', 1), ('包周群', 1), ('一周number', 2), ('保', 2), ('w站日', 2), ('量提升number以上', 2), ('包月群', 1), ('一月number', 1), ('n套群', 1), ('搜索', 2), ('w路', 5), ('衍', 1), ('你自己', 1), ('碛幸惶淄晟频碾', 1), ('件群', 1), ('严到y', 1), ('有意者请来信联系', 1), ('yhzxnumber', 3), ('二', 1), ('搜索引擎', 1), ('缘顷', 1), ('超值服', 1), ('眨', 1), ('特惠', 1), ('y河之星', 3), ('湍', 2), ('登', 1), ('全球number', 1), ('英文搜索引擎', 1), ('中文搜索引擎', 1), ('你的', 1), ('w站享', 1), ('u全球', 1), ('mail地址在各地', 1), ('的分', 1), ('盗', 1), ('香港', 1), ('场', 1), ('全球', 1), ('韧馍', 1), ('有效的email位址', 1), ('y料', 1), ('欤', 1), ('可以根', 1), ('客', 1), ('舻男枰', 1), ('在指定的地方', 1), ('域', 1), ('行', 1), ('e等', 1), ('傩酝斗烹', 1), ('如', 1), ('你想', 1), ('某地number', 1), ('f人一天之', 1), ('都知道你的', 1), ('a品', 1), ('那麽', 1), ('件投放就是你最好的解', 1), ('q方案', 1), ('它的', 1), ('r格是所有', 1), ('髅', 1), ('r格中最低', 1), ('但又是最有效的', 1), ('c遇每', 1), ('r每刻都等待著你', 1), ('也', 1), ('s你正是用了', 1), ('拥', 1), ('髅菏侄危', 1), ('而打垮了所有的', 1), ('κ帧ｚs快行', 1), ('影桑', 1), ('你的企', 1), ('公司立即掌握商', 1), ('髅降拿', 1), ('v告e', 1), ('是普通', 1), ('麂n', 1), ('i所不能比', 1), ('m的', 1), ('m及付款方式', 1), ('本公司', 1), ('氖戮w路', 1), ('v告多年', 1), ('宣', 1), ('髁χ任痪又', 1), ('第四大', 1), ('v告商之一', 1), ('我', 2), ('有', 1), ('ｉt的技', 1), ('g人', 1), ('t和高速', 1), ('i的服', 1), ('障到y', 1), ('氖麓隧', 1), ('工作', 1), ('必', 1), ('您的企', 1), ('i提供最迅捷', 1), ('最有效', 1), ('最', 1), ('m意的服', 1), ('铡', 2), ('需求客', 1), ('粽', 1), ('暨', 1), ('箱', 1), ('硇耪', 1), ('注明您需要的服', 1), ('张c', 1), ('m方式', 1), ('快回', 1), ('港', 1), ('澳', 1), ('地', 3), ('付款方式', 1), ('任何', 1), ('家均可到', 1), ('y行', 4), ('number天到', 2), ('ぃ', 2), ('需到', 1), ('k理', 1), ('benificiary', 2), ('wangjingjing', 1), ('mianyang', 1), ('中文', 1), ('φ眨', 1), ('收', 1), ('款', 1), ('人', 1), ('王晶晶', 1), ('收款', 1), ('中', 2), ('分行', 1), ('收款人', 1), ('款到後我', 1), ('⒘⒓', 1), ('您提供最', 1), ('m意服', 1), ('g迎', 2), ('硇', 2), ('黼', 2), ('有限公司', 1), ('公司地址', 1), ('大', 1), ('公司服', 1), ('number福壕w路推', 1), ('w站建', 1), ('名', 1), ('寄存', 1), ('功能', 1), ('得', 1), ('q空', 1), ('l展等', 1), ('x低廉', 1), ('服', 1)])

Saving and loading the tokenizer for future inference_____no_output_____

import pickle

# saving
with open('tokenizer.pickle', 'wb') as handle:
    pickle.dump(t, handle, protocol=pickle.HIGHEST_PROTOCOL)_____no_output_____vocab=list(t.word_index.keys())
vocab_size=len(t.word_index)+1
max_length=max([len(s.split(" ")) for s in X])
print("Maximum length: ", max_length)
print("Vocabulary size:", vocab_size)Maximum length:  13305
Vocabulary size: 33674
vocab[:20]_____no_output_____def save_vocab(words, filename):
  data = '\n'.join(words)
  file = open(filename, 'w')
  file.write(data)
  file.close()

save_vocab(vocab, 'vocab.txt')_____no_output_____def load_vocab(filename): # function to load the vocabulary from a file
  file = open(filename, 'r')
  text = file.read()# read all text
  vocab = text.split()
  vocab = list(set(vocab))
  file.close() # close the file
  return vocab

# load vocabulary
vocab_filename = 'vocab.txt'
vocab = load_vocab(vocab_filename)_____no_output_____

## Generate word embeddings_____no_output_____

from numpy import asarray
from numpy import zeros
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from keras.layers import Embedding_____no_output_____#loading glove embeddings from Kaggle as I am running this on Google Colab - change according to your source
import os
os.environ['KAGGLE_USERNAME'] = "pogayo" # username from the json file
os.environ['KAGGLE_KEY'] = "08102192841cd4c94b1dc524e620689b" # key from the json file
!kaggle datasets download -d danielwillgeorge/glove6b100dtxt # api copied from kaggleDownloading glove6b100dtxt.zip to /content
 98% 129M/131M [00:10<00:00, 15.3MB/s]
100% 131M/131M [00:10<00:00, 12.7MB/s]
!unzip -q glove6b100dtxt.zip_____no_output_____def create_embeddings_index(filename):
  embeddings_index = dict()
  f = open( filename )
  for line in f:
    values = line.split()
    word = values[0]
    coefs = asarray(values[1:], dtype= 'float32' )
    embeddings_index[word] = coefs
  f.close()
  return embeddings_index_____no_output_____def create_embedding_matrix(tokenizer, embeddings_index):
  vocab_size=len(tokenizer.word_index)+1
  embedding_matrix = zeros((vocab_size, 100))
  for word, i in tokenizer.word_index.items():
    embedding_vector = embeddings_index.get(word)
    if embedding_vector is not None:
        embedding_matrix[i] = embedding_vector
  return embedding_matrix

_____no_output_____embeddings_index = create_embeddings_index('glove.6B.100d.txt')
print( ' Loaded %s word vectors. ' % len(embeddings_index)) Loaded 400000 word vectors.
embedding_matrix=create_embedding_matrix(t, embeddings_index)_____no_output_____t.word_index_____no_output_____# Sequencing and padding on train and test sets
max_len=50 #change accordingly
training_sequences = t.texts_to_sequences(X_train)
training_padded = pad_sequences (training_sequences, maxlen = max_len, padding = 'post' )

testing_sequences = t.texts_to_sequences(X_test)
testing_padded = pad_sequences(testing_sequences, maxlen = max_len,
padding = 'post')_____no_output_____

####Save a shorter embeddings file with only the words in our training vocabulary_____no_output_____

trimmed_embeddings=[]
f=open("embeddings.txt", 'w+')
for word, i in t.word_index.items():
    embedding_vector = embeddings_index.get(word)
    line=word
    if embedding_vector is not None:
      embedding_string=[str(n) for n in embedding_vector]
      line=line+' '+' '.join(embedding_string)+"\n"
    f.write(line)
f.close()_____no_output_____

######final function that puts everything together and outputs tokens ready for training/testing
_____no_output_____

def preprocess(data, tokenizer_filename, emb_filename, vocab_size= 33674,):

  """
  data is a pandas series object
  """
  # if (type(data)!='pandas.core.series.Series'):
  #   print("Please enter a series object")

  from keras.preprocessing.text import Tokenizer
  from numpy import asarray
  from numpy import zeros
  from keras.preprocessing.text import Tokenizer
  from keras.preprocessing.sequence import pad_sequences

  # loading
  with open(tokenizer_filename, 'rb') as handle:
    tokenizer = pickle.load(handle)

  # fit the tokenizer on the documents
  data=data.apply(str)

  embedding_index=create_embeddings_index(emb_filename)
  embedding_matrix=create_embedding_matrix(tokenizer, embeddings_index)

  max_len=50 #change accordingly
  test_sequences = tokenizer.texts_to_sequences(data)
  test_padded = pad_sequences(test_sequences, maxlen = max_len, padding = 'post' )

  return test_padded_____no_output_____test_tokens=preprocess(pd.Series(df.iloc[2999, 0]), "tokenizer.pickle", "embeddings.txt")_____no_output_____test_tokens_____no_output_____max_len_____no_output_____

####Finally, create the embedding layer_____no_output_____

#Create an embedding layer
e = Embedding(vocab_size, 100, weights=[embedding_matrix], input_length=max_len, trainable=False)_____no_output_____

##Defining the Deep Neural Network_____no_output_____

from keras.models import Sequential
from keras.layers import Dense, LSTM, Flatten, Embedding_____no_output_____#LSTM hyperparameters
n_lstm = 20
drop_lstm =0.2_____no_output_____#LSTM Spam detection architecture
model= Sequential()
model.add(e)
model.add(LSTM(n_lstm, dropout=drop_lstm, return_sequences=True))
model.add(LSTM(n_lstm, dropout=drop_lstm))
model.add(Dense(1, activation='sigmoid'))_____no_output_____initial_weights = model.get_weights(); #Will be useful in fine-tuning as we can compare different hyperparameters_____no_output_____model.compile(loss = 'binary_crossentropy', optimizer = 'adam', metrics=['accuracy'])_____no_output_____

##Model Training_____no_output_____

from keras.callbacks import EarlyStopping_____no_output_____model.summary()Model: "sequential_3"
_________________________________________________________________
Layer (type)                 Output Shape              Param #
=================================================================
embedding (Embedding)        (None, 50, 100)           3367400
_________________________________________________________________
lstm_6 (LSTM)                (None, 50, 20)            9680
_________________________________________________________________
lstm_7 (LSTM)                (None, 20)                3280
_________________________________________________________________
dense_3 (Dense)              (None, 1)                 21
=================================================================
Total params: 3,380,381
Trainable params: 12,981
Non-trainable params: 3,367,400
_________________________________________________________________
num_epochs = 10
early_stop = EarlyStopping(monitor='val_loss', patience=2)_____no_output_____model.fit(training_padded, y_train, epochs=num_epochs, validation_split=0.3,callbacks =[early_stop], verbose=2)Epoch 1/10
53/53 - 3s - loss: 0.5035 - accuracy: 0.8226 - val_loss: 0.4184 - val_accuracy: 0.8500
Epoch 2/10
53/53 - 2s - loss: 0.4065 - accuracy: 0.8315 - val_loss: 0.2713 - val_accuracy: 0.8917
Epoch 3/10
53/53 - 2s - loss: 0.2500 - accuracy: 0.9060 - val_loss: 0.1881 - val_accuracy: 0.9264
Epoch 4/10
53/53 - 2s - loss: 0.1647 - accuracy: 0.9488 - val_loss: 0.1654 - val_accuracy: 0.9472
Epoch 5/10
53/53 - 2s - loss: 0.1585 - accuracy: 0.9440 - val_loss: 0.1348 - val_accuracy: 0.9444
Epoch 6/10
53/53 - 2s - loss: 0.1099 - accuracy: 0.9667 - val_loss: 0.1021 - val_accuracy: 0.9653
Epoch 7/10
53/53 - 2s - loss: 0.0865 - accuracy: 0.9726 - val_loss: 0.1109 - val_accuracy: 0.9653
Epoch 8/10
53/53 - 2s - loss: 0.0889 - accuracy: 0.9696 - val_loss: 0.1321 - val_accuracy: 0.9583
res=model.predict_classes(test_tokens)
res_____no_output_____res.flatten()_____no_output_____res.flatten()[0]_____no_output_____res[0][0]_____no_output_____

## Model evaluation & hyperparameter tuning_____no_output_____### First, we will test the default model that we created_____no_output_____

model.evaluate(testing_padded, y_test)19/19 [==============================] - 0s 8ms/step - loss: 0.1961 - accuracy: 0.9321
def gen_model(embedding,n_neurons, drop_lstm ):
    model= Sequential()
    model.add(embedding)
    model.add(LSTM(n_neurons, dropout=drop_lstm, return_sequences=True))
    model.add(LSTM(n_neurons, dropout=drop_lstm, return_sequences=True))
    model.add(Dense(1, activation='sigmoid'))
    return model_____no_output_____

###Then we will test early stopping so that we do not stop training too early

---

_____no_output_____

def fit_lstm_early(X_train, y_train, embedding, n_lstm, drop_lstm, num_epochs, early_stop):
  train_acc=[]
  test_acc=[]

  for i in early_stop:
    # prepare model
    model= gen_model(embedding, n_lstm, drop_lstm )
    model.compile(loss = 'binary_crossentropy', optimizer = 'adam', metrics=['accuracy'])

    print("Early stopping patience: ", i)
    early_stop = EarlyStopping(monitor='val_loss', patience=i)
    model.fit(X_train, y_train, epochs=num_epochs, validation_split=0.3, callbacks =[early_stop], verbose=0)
		# evaluate model on train data
    train_acc.append(model.evaluate(X_train, y_train))
		# evaluate model on test data
    test_acc.append(model.evaluate(testing_padded, y_test))
    model.reset_states()
  history =pd.DataFrame(train_acc,columns=['train_loss','train_acc' ] )
  test_history =pd.DataFrame(test_acc,columns=['test_loss','test_acc' ] )
  history=history.join(test_history)
  return history_____no_output_____fit_lstm_early(training_padded, y_train, e,n_lstm, drop_lstm, 20,[2,3,4,5])Early stopping patience:  2
75/75 [==============================] - 1s 10ms/step - loss: 0.1478 - accuracy: 0.9463
19/19 [==============================] - 0s 9ms/step - loss: 0.1864 - accuracy: 0.9370
Early stopping patience:  3
75/75 [==============================] - 1s 9ms/step - loss: 0.1139 - accuracy: 0.9575
19/19 [==============================] - 0s 9ms/step - loss: 0.1842 - accuracy: 0.9322
Early stopping patience:  4
75/75 [==============================] - 1s 9ms/step - loss: 0.1174 - accuracy: 0.9586
19/19 [==============================] - 0s 9ms/step - loss: 0.1932 - accuracy: 0.9324
Early stopping patience:  5
75/75 [==============================] - 1s 9ms/step - loss: 0.1618 - accuracy: 0.9370
19/19 [==============================] - 0s 9ms/step - loss: 0.2551 - accuracy: 0.9113

Early stopping after 2 steps seems to have the highest test accuracy. So we will maintain it.
###Second, we will test the number of neurons in the lstm layers_____no_output_____

def fit_lstm_nodes(X_train, y_train, embedding, n_lstm, drop_lstm, num_epochs, early_stop):
  train_acc=[]
  test_acc=[]
  early_stopping = EarlyStopping(monitor='val_loss', patience=early_stop)

  for i in n_lstm:
    # prepare model

    model= Sequential()
    model.add(embedding)
    model.add(LSTM(i, dropout=drop_lstm, return_sequences=True))
    model.add(LSTM(i, dropout=drop_lstm, return_sequences=True))
    model.add(Dense(1, activation='sigmoid'))

    model.compile(loss = 'binary_crossentropy', optimizer = 'adam', metrics=['accuracy'])

    print("Number of nodes: ", i)
    model.fit(X_train, y_train, epochs=num_epochs, validation_split=0.3, callbacks =[early_stopping], verbose=0)
		# evaluate model on train data
    train_acc.append(model.evaluate(X_train, y_train))
		# evaluate model on test data
    test_acc.append(model.evaluate(testing_padded, y_test))
    model.reset_states()
  history =pd.DataFrame(train_acc,columns=['train_loss','train_acc' ] )
  test_history =pd.DataFrame(test_acc,columns=['test_loss','test_acc' ] )
  history=history.join(test_history)

  return history_____no_output_____fit_lstm_nodes(training_padded, y_train, e,[10, 20, 30, 40], drop_lstm, 20,2)Number of nodes:  10
75/75 [==============================] - 1s 8ms/step - loss: 0.1706 - accuracy: 0.9426
19/19 [==============================] - 0s 7ms/step - loss: 0.2084 - accuracy: 0.9288
Number of nodes:  20
75/75 [==============================] - 1s 9ms/step - loss: 0.1659 - accuracy: 0.9418
19/19 [==============================] - 0s 9ms/step - loss: 0.2090 - accuracy: 0.9288
Number of nodes:  30
75/75 [==============================] - 1s 10ms/step - loss: 0.1337 - accuracy: 0.9496
19/19 [==============================] - 0s 10ms/step - loss: 0.1928 - accuracy: 0.9342
Number of nodes:  40
75/75 [==============================] - 1s 12ms/step - loss: 0.1203 - accuracy: 0.9568
19/19 [==============================] - 0s 12ms/step - loss: 0.2018 - accuracy: 0.9342

30 neurons seems to have the best test accuracy so we will use that for our final model

###Next, we will try and see the best optimizer_____no_output_____

def fit_lstm_optimizer(X_train, y_train, embedding, n_lstm, drop_lstm, num_epochs, early_stop, optimizer):
  train_acc=[]
  test_acc=[]
  early_stopping = EarlyStopping(monitor='val_loss', patience=early_stop)

  model= Sequential()
  model.add(embedding)
  model.add(LSTM(n_lstm,dropout=drop_lstm, return_sequences=True))
  model.add(LSTM(n_lstm, dropout=drop_lstm, return_sequences=True))
  model.add(Dense(1, activation='sigmoid'))

  model.compile(loss = 'binary_crossentropy', optimizer = optimizer, metrics=['accuracy'])
  model.fit(X_train, y_train, epochs=num_epochs, validation_split=0.3, callbacks =[early_stopping], verbose=0)
  # evaluate model on train data
  train_acc.append(model.evaluate(X_train, y_train))
  # evaluate model on test data
  test_acc.append(model.evaluate(testing_padded, y_test))
  model.reset_states()
  history =pd.DataFrame(train_acc,columns=['train_loss','train_acc' ] )
  test_history =pd.DataFrame(test_acc,columns=['test_loss','test_acc' ] )
  history=history.join(test_history)
  return history_____no_output_____fit_lstm_optimizer(training_padded, y_train, e, 20, drop_lstm, 20,2,'SGD')75/75 [==============================] - 1s 10ms/step - loss: 0.4289 - accuracy: 0.8333
19/19 [==============================] - 0s 9ms/step - loss: 0.4317 - accuracy: 0.8333
fit_lstm_optimizer(training_padded, y_train, e, 20, drop_lstm, 20,2,'RMSprop')75/75 [==============================] - 1s 9ms/step - loss: 0.2733 - accuracy: 0.9003
19/19 [==============================] - 0s 9ms/step - loss: 0.2909 - accuracy: 0.8971
fit_lstm_optimizer(training_padded, y_train, e, 20, drop_lstm, 20,2,'adam')75/75 [==============================] - 1s 9ms/step - loss: 0.1756 - accuracy: 0.9346
19/19 [==============================] - 0s 9ms/step - loss: 0.2159 - accuracy: 0.9253
fit_lstm_optimizer(training_padded, y_train, e, 20, drop_lstm, 20,2,'Adagrad')75/75 [==============================] - 1s 9ms/step - loss: 0.4720 - accuracy: 0.8339
19/19 [==============================] - 0s 8ms/step - loss: 0.4741 - accuracy: 0.8333
#Finally, we will try to_____no_output_____

Adam optimizer wins
Build Our final optimized model

#Finally, we will try add more layers_____no_output_____

import keras
def gen_lstm_layers(model, number, n_neurons, activation, drop_lstm):
  for i in range(2, number):
    model.add(LSTM( n_neurons, dropout=drop_lstm, return_sequences=True))
  return model
_____no_output_____def fit_lstm_layers(X_train, y_train, embedding, n_lstm, drop_lstm, num_epochs, early_stop, n_layers):
  train_acc=[]
  test_acc=[]
  early_stopping = EarlyStopping(monitor='val_loss', patience=early_stop)

  for i in n_layers:
    # prepare model

    model= Sequential()
    model.add(embedding)
    model=gen_lstm_layers(mod, i,n_neurons, "relu", drop_lstm)
    model.add(LSTM(i, dropout=drop_lstm, return_sequences=True))
    model.add(Dense(1, activation='sigmoid'))

    model.compile(loss = 'binary_crossentropy', optimizer = 'adam', metrics=['accuracy'])

    print("Number of layers: ", i)
    model.fit(X_train, y_train, epochs=num_epochs, validation_split=0.3, callbacks =[early_stopping], verbose=0)
		# evaluate model on train data
    train_acc.append(model.evaluate(X_train, y_train))
		# evaluate model on test data
    test_acc.append(model.evaluate(testing_padded, y_test))
    model.reset_states()
  history =pd.DataFrame(train_acc,columns=['train_loss','train_acc' ] )
  test_history =pd.DataFrame(test_acc,columns=['test_loss','test_acc' ] )
  history=history.join(test_history)

  return history_____no_output_____fit_lstm_layers(training_padded, y_train, e, 20, drop_lstm, 20,2,[2, 3, 4, 5])Number of layers:  2
75/75 [==============================] - 2s 26ms/step - loss: 0.4201 - accuracy: 0.8514
19/19 [==============================] - 0s 26ms/step - loss: 0.4250 - accuracy: 0.8480
Number of layers:  3
75/75 [==============================] - 2s 33ms/step - loss: 0.4272 - accuracy: 0.8333
19/19 [==============================] - 1s 34ms/step - loss: 0.4322 - accuracy: 0.8333
Number of layers:  4
75/75 [==============================] - 3s 46ms/step - loss: 0.4369 - accuracy: 0.8333
19/19 [==============================] - 1s 46ms/step - loss: 0.4376 - accuracy: 0.8333
Number of layers:  5
75/75 [==============================] - 5s 64ms/step - loss: 0.4513 - accuracy: 0.8333
19/19 [==============================] - 1s 62ms/step - loss: 0.4513 - accuracy: 0.8333

Two layers won
##Build the final model_____no_output_____

n_neurons=50
optimizer="adam"
early_stop=2
num_epochs=20_____no_output_____model= Sequential()
model.add(e)
model.add(LSTM(n_neurons, dropout=drop_lstm, return_sequences=True))
model.add(LSTM(n_neurons, dropout=drop_lstm))
model.add(Dense(1, activation='sigmoid'))

early_stop = EarlyStopping(monitor='val_loss', patience=early_stop)

model.compile(loss = 'binary_crossentropy', optimizer = optimizer, metrics=['accuracy'])_____no_output_____model.fit(training_padded, y_train, epochs=num_epochs, validation_split=0.3,callbacks =[early_stop], verbose=2)Epoch 1/20
53/53 - 4s - loss: 0.4820 - accuracy: 0.8232 - val_loss: 0.3834 - val_accuracy: 0.8500
Epoch 2/20
53/53 - 3s - loss: 0.3342 - accuracy: 0.8601 - val_loss: 0.2338 - val_accuracy: 0.9319
Epoch 3/20
53/53 - 3s - loss: 0.1867 - accuracy: 0.9226 - val_loss: 0.1516 - val_accuracy: 0.9431
Epoch 4/20
53/53 - 3s - loss: 0.1181 - accuracy: 0.9601 - val_loss: 0.0972 - val_accuracy: 0.9653
Epoch 5/20
53/53 - 3s - loss: 0.1091 - accuracy: 0.9619 - val_loss: 0.0995 - val_accuracy: 0.9653
Epoch 6/20
53/53 - 3s - loss: 0.0871 - accuracy: 0.9696 - val_loss: 0.1234 - val_accuracy: 0.9486
model.evaluate(testing_padded, y_test)19/19 [==============================] - 0s 14ms/step - loss: 0.1110 - accuracy: 0.9583
model.save('spam2.h5')_____no_output_____http://localhost:5000/predict?_____no_output_____!cp embeddings.txt  "/content/drive/MyDrive/Year 4/Machine Learning/"_____no_output_____!cp spam_or_not_spam.csv.zip "/content/drive/MyDrive/Year 4/Machine Learning/spam_or_not_spam.csv.zip"_____no_output_____