import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
# 로이터 뉴스 데이터 임포트
from tensorflow.keras.datasets import reuters
data = reuters.load_data()
(X_train, y_train), (X_test, y_test) = data
# 뉴스기사 8982개
print(X_train.shape)
print(y_train.shape)
print(X_test.shape)
print(y_test.shape)(8982,)
(8982,)
(2246,)
(2246,)
# 실제 뉴스 기사의 단어들을 다 숫자로 변경시켜서 저장해둔 데이터
# 현재 로이터 뉴스 기사 데이터셋은 레이블인코딩으로 인코딩을 해준 상태
# 레이블 인코딩을 하면서 가장 빈도수가 높은 순위로 숫자가 매겨져 있음
#(1이 가장 많이 나온 단어, 숫자가 커질수록 빈도수가 적은 단어)
X_train[0]
# -> 단어가 많은 경우 원핫인코딩을 사용하면 한 단어를 제외하고 모두 0으로 표시되기
# 때문에 저장 공간측면에서 매우 비효율적임
# -> 텍스트나 자연어 처리 분야에서는 문제 데이터에 레이블 인코딩을 활용하면
# 단어의 빈도수까지 고려해줄 수 있음[1,
27595,
28842,
8,
43,
10,
447,
5,
25,
207,
270,
5,
3095,
111,
16,
369,
186,
90,
67,
7,
89,
5,
19,
102,
6,
19,
124,
15,
90,
67,
84,
22,
482,
26,
7,
48,
4,
49,
8,
864,
39,
209,
154,
6,
151,
6,
83,
11,
15,
22,
155,
11,
15,
7,
48,
9,
4579,
1005,
504,
6,
258,
6,
272,
11,
15,
22,
134,
44,
11,
15,
16,
8,
197,
1245,
90,
67,
52,
29,
209,
30,
32,
132,
6,
109,
15,
17,
12]
# 첫번째 뉴스기사는 87개의 단어로 이루어져있음
len(X_train[0])87
# 로이터 뉴스기사의 단어들 별로 어떤 숫자로 인코딩 되어 있는지 확인
news_words = reuters.get_word_index()
단어의 빈도수에 따라 딕셔너리 데이터들을 정렬
# 딕셔너리는 순서가 없고 key값으로만 접근이 가능하기 때문에 value값도 같이 활용하기 위해 items 명령을 사용함
# items : 딕셔너리 형태의 key, value값을 튜플로 변경하여 dict_items 자료형태로 만들어주는 명령
news_words.items()
# 딕셔너리를 리스트로 만들려면 번거로운데 이를 줄이면서 정렬이나 반복문에 적용할 수 있는
# dict_items 형태로 변환하여 주로 사용함
기사가 어떤 단어들로 구성되어 있는지 이어붙여서 확인해보자
# 딕셔너리에서 key값과 value값을 체인지
word_of_news = {}
for key, value in news_words.items():
# 비어있는 딕셔너리에 값을 새롭게 넣는데 key값과 value값을 반대로 채워줌
# (인코딩된 숫자로 접근해서 문자를 뽑아와서 문장으로 만들기 편하게 해주기 위함!)
word_of_news[value] = key
print(word_of_news)
print('key값이 1번인 단어 :', word_of_news[1]){10996: 'mdbl', 16260: 'fawc', 12089: 'degussa', 8803: 'woods', 13796: 'hanging', 20672: 'localized', 20673: 'sation', 20675: 'chanthaburi', 10997: 'refunding', 8804: 'hermann', 20676: 'passsengers', 20677: 'stipulate', 8352: 'heublein', 20713: 'screaming', 16261: 'tcby', 185: 'four', 1642: 'grains', 20680: 'broiler', 12090: 'wooden', 1220: 'wednesday', 13797: 'highveld', 7593: 'duffour', 20681: '0053', 3914: 'elections', 2563: '270', 3551: '271', 5113: '272', 3552: '273', 3400: '274', 7975: 'rudman', 3401: '276', 3478: '277', 3632: '278', 4309: '279', 9381: 'dormancy', 7247: 'errors', 3086: 'deferred', 20683: 'sptnd', 8805: 'cooking', 20684: 'stratabit', 16262: 'designing', 20685: 'metalurgicos', 13798: 'databank', 20686: '300er', 20687: 'shocks', 7972: 'nawg', 20688: 'tnta', 20689: 'perforations', 2891: 'affiliates', 20690: '27p', 16263: 'ching', 595: 'china', 16264: 'wagyu', 3189: 'affiliated', 16265: 'chino', 16266: 'chinh', 20692: 'slickline', 13799: 'doldrums', 12092: 'kids', 3028: 'climbed', 6693: 'controversy', 20693: 'kidd', 12093: 'spotty', 12639: 'rebel', 9382: 'millimetres', 4007: 'golden', 5689: 'projection', 12094: 'stern', 7903: "hudson's", 10066: 'dna', 20695: 'dnc', 20696: 'hodler', 2394: 'lme', 20697: 'insolvancy', 13800: 'music', 1984: 'therefore', 10998: 'dns', 6959: 'distortions', 13801: 'thassos', 20698: 'populations', 8806: 'meteorologist', 43: 'loss', 9383: 'exco', 20813: 'adventist', 16267: 'murchison', 10999: 'locked', 13802: 'kampala', 20699: 'arndt', 1267: 'nakasone', 20700: 'steinweg', 3633: "india's", 3029: 'wang', 10067: 'wane', 13803: 'unjust', 13804: 'titanium', 850: 'want', 20701: 'pinto', 16268: "institutes'", 7973: 'absolute', 4677: 'travel', 6422: 'cutback', 16269: 'nazmi', 1858: 'modest', 16270: 'shopwell', 20702: 'sedi', 20703: 'adoped', 16271: 'tulis', 20704: '18th', 20705: "wmc's", 20706: 'menlo', 11000: 'reiners', 12095: 'farmlands', 20707: 'nonsensical', 20708: 'elisra', 2461: 'welcomed', 20709: 'peup', 16272: "holiday's", 20711: 'activating', 16273: 'avondale', 16274: 'interational', 20712: 'welcomes', 16275: 'fip', 11001: 'tailings', 4205: 'fit', 16276: 'lifeline', 1916: 'bringing', 4819: 'fix', 6164: '624', 12096: 'naturalite', 6165: 'wales', 8807: 'fin', 11129: 'fio', 20714: 'ceremenony', 20715: 'sovr', 20716: "yeo's", 1788: 'effects', 13805: 'sixteen', 8808: 'undeveloped', 13806: 'glutted', 20717: 'barton', 20718: 'froday', 10089: 'arrow', 11002: 'stabilises', 6960: 'allan', 20719: '374p', 3891: '393', 4008: '392', 4206: '391', 3079: '390', 4550: '397', 6166: '396', 6423: '395', 4207: '394', 6961: '399', 4208: '398', 7595: 'stabilised', 5114: 'smelters', 20720: 'oprah', 20721: 'orginially', 20722: "tvx's", 16278: 'ponomarev', 20723: 'enviroment', 20724: "reeves'", 8363: 'mason', 1670: 'encourage', 7596: 'adapt', 12776: 'abbott', 13808: 'stamping', 20726: 'colquiri', 11003: 'ambrit', 8353: 'strata', 4821: 'corrects', 11922: 'sandra', 859: 'estimate', 20727: 'universally', 20728: 'chlorine', 16279: 'competes', 10068: 'leiner', 8809: 'ministries', 8810: 'disturbed', 13809: 'competed', 8811: 'juergen', 13810: 'kfw', 11004: 'turben', 9384: 'reintroduced', 20729: 'maladies', 4101: 'chevron', 16280: 'lazere', 8812: 'antilles', 11907: 'dti', 9070: 'specially', 4678: 'bilzerian', 13811: 'bakelite', 20730: 'renovated', 568: 'service', 16281: 'payless', 20731: 'spiegler', 831: 'needed', 16282: 'wigglesworth', 6962: 'master', 13812: 'antonson', 20732: 'genesis', 13813: 'vismara', 20734: 'organically', 20735: "accords'", 5940: 'task', 7974: 'positively', 3479: 'feasibility', 6963: 'ahmed', 13814: "suralco's", 20736: 'awacs', 16283: 'idly', 20737: 'regulator', 12097: 'pseudorabies', 16284: 'staubli', 8813: 'nzi', 5115: 'feeling', 3127: '275', 20738: '6819', 16285: 'gorman', 8354: 'sustaining', 9385: 'spectrum', 20739: 'consenting', 12098: 'recapitalized', 11562: 'sailed', 7597: 'dozen', 1985: 'affairs', 2253: 'courier', 8355: 'kremlin', 895: 'shipments', 16286: "aquino's", 10070: 'committing', 5293: 'sugarcane', 9386: 'diminishing', 16287: 'vexing', 11005: 'simplify', 6167: 'mouth', 7248: 'steinhardt', 8814: 'conceded', 9387: 'bradford', 7976: 'singer', 20740: '5602', 13816: "1987's", 4950: 'tech', 6424: 'teck', 20741: 'majv', 666: 'saying', 16477: 'dickey', 20742: 'sweetner', 21149: 'teresa', 20743: 'ulcer', 13817: 'cheaply', 2361: 'thai', 6964: 'orleans', 16290: 'excavator', 6168: 'rico', 12099: 'lube', 13818: 'rick', 4679: 'rich', 13819: 'kerna', 950: 'rice', 4209: 'rica', 5503: 'plate', 16291: 'platt', 8356: 'altogether', 8815: 'jaguar', 20744: 'dynair', 8816: 'patch', 2892: 'ldp', 13820: 'boarded', 16292: 'precluding', 11006: 'clarified', 16293: 'sensitivity', 1511: 'alternative', 11007: 'clarifies', 5116: 'lots', 7598: 'irs', 20745: 'irv', 13821: 'iri', 13822: 'ira', 5690: 'timber', 20746: 'ire', 5219: 'discipline', 1937: 'extend', 3634: 'nature', 16295: "amb's", 16296: 'dunhill', 2142: 'extent', 20747: 'restrcitions', 2396: 'heating', 11008: "mannesmann's", 20748: 'outsanding', 20749: 'multimillions', 13824: 'sarcinelli', 6694: 'southeastern', 10071: 'eradicate', 9388: 'libyan', 20750: 'foreclosing', 12101: 'maclaine', 20751: 'fra', 353: 'union', 11009: 'frn', 386: 'much', 12102: 'fry', 20752: 'mothball', 10072: 'chlorazepate', 12103: 'dxns', 19981: 'toyko', 20753: 'spit', 16297: '007050', 16298: 'freehold', 13825: 'davy', 11010: 'dave', 12177: 'spie', 10117: 'aguayo', 12104: 'wildcat', 10069: 'fecs', 20754: 'kennan', 16299: 'intal', 9389: 'contingencies', 16551: 'professionally', 16300: 'microbiological', 20756: 'misconstrued', 409: 'k', 20757: 'securitiesd', 16301: 'deferring', 5941: 'kohl', 3030: 'conditioned', 20758: 'fnhb', 16302: "october's", 13954: 'memorial', 6965: 'democracies', 27520: 'conformed', 464: 'split', 12105: "bond's", 11112: 'thinly', 16515: 'dunkirk', 16303: 'cavanaugh', 13827: "securities'", 21345: 'marches', 16304: 'issam', 2020: 'workforce', 12106: 'meinert', 13828: 'boiler', 5294: "bp's", 16305: 'torpedoed', 20762: 'indidate', 13829: 'downwardly', 20763: 'viviez', 20764: 'vladiminovich', 16306: 'academic', 20765: 'architecural', 1117: 'corporate', 16307: 'appropriately', 20766: 'teicc', 20767: "hanover's", 8817: 'aristech', 20768: 'portrayed', 21383: 'raffineries', 20770: 'hai', 7599: 'hal', 13830: 'ham', 10073: 'han', 20771: 'e15b', 61: 'had', 20772: 'hay', 13831: 'botchwey', 10074: 'haq', 37: 'has', 13832: 'hat', 20773: 'hav', 20774: 'fortin', 8818: 'municipal', 20775: 'osman', 20776: 'fsical', 3480: 'elders', 12107: 'survival', 16308: 'unequivocally', 2519: 'objective', 6695: 'indicative', 10075: 'shadow', 21411: 'riskiness', 20778: 'positiive', 10076: "american's", 16309: 'alick', 16310: 'harima', 12108: 'alice', 20779: 'altschul', 16311: 'festivities', 20780: 'medecines', 2942: 'beneficial', 12109: 'yoweri', 13833: 'crowd', 9390: 'crowe', 3553: 'crown', 13679: 'topping', 8819: 'captive', 12110: 'billboard', 6169: 'fiduciary', 3402: 'bottom', 20782: 'plucked', 20783: 'locksmithing', 9391: 'ecopetrol', 24018: 'pipestone', 5505: "growers'", 20785: 'borrows', 16312: 'eduard', 13834: 'venpres', 16313: 'bamboo', 13835: 'foolish', 20786: 'uruguyan', 20787: 'officeholders', 20788: 'economiques', 16314: 'aden', 4822: 'maxwell', 4680: 'marshall', 16315: 'honeymoon', 16316: 'administer', 20790: 'shoots', 16317: 'rubbertech', 16318: 'johsen', 10077: 'reciprocity', 13836: 'fabric', 20791: 'suffice', 20792: 'spokemsan', 20793: "sonora's", 16319: '5865', 16320: "systems'", 20794: 'perfumes', 20795: 'halycon', 20796: 'nonvoting', 7250: 'safeguard', 21538: 'sawdust', 20797: "else's", 13837: 'arrays', 20798: 'aza', 20799: 'smasher', 12111: 'complications', 1813: 'pesos', 20800: 'relabelling', 3722: 'passenger', 12112: "avon's", 20801: 'megahertz', 10683: 'mirror', 8357: 'minas', 16322: 'bourdain', 20802: 'crownx', 6425: 'eventual', 1207: 'crowns', 1369: 'role', 20803: 'obliges', 16323: 'rolf', 13838: 'vegetative', 20804: 'rolm', 4419: 'roll', 2463: 'intend', 16324: 'palms', 19255: 'denys', 13839: 'transported', 20805: 'moresby', 16325: 'devon', 1351: 'intent', 20806: "camco's", 5942: 'variable', 20807: 'transporter', 16326: 'danske', 13840: 'friedhelm', 8358: 'hawker', 17774: "sand's", 20808: 'preseving', 12113: '80386', 16328: 'bnls', 19984: 'ordination', 11011: 'overturned', 16329: 'erred', 6696: 'cincinnati', 16710: 'corps', 20809: 'whoever', 16330: 'osp', 13841: 'osr', 12114: 'ost', 16331: 'chair', 5647: '690', 20810: 'grapples', 13842: 'megawatts', 20811: 'photocopiers', 20812: 'sconninx', 2274: 'circumstances', 13843: 'oversight', 20814: "paradyne's", 6363: '691', 20815: 'paychecks', 13844: "stadelmann's", 3241: 'choice', 11012: 'vastagh', 8820: 'embark', 9392: 'gloomy', 9393: 'stays', 4009: 'exact', 5117: 'minute', 11892: 'kittiwake', 20816: 'picul', 20817: 'skewed', 11013: 'cooke', 10078: 'defaults', 11014: 'reimpose', 9394: 'hindered', 20818: 'lengthened', 16333: 'chopping', 13845: 'mckiernan', 20819: 'collaspe', 7251: 'corazon', 7600: 'antwerp', 13846: 'abdullah', 13847: 'goldston', 442: '300', 20821: 'cassa', 20822: 'casse', 4081: '695', 2979: 'ground', 839: 'boost', 16334: 'azusa', 9395: 'drafted', 4823: '303', 13848: 'climbs', 7601: 'honour', 20823: 'vanderbilt', 3968: '305', 3031: 'address', 8821: 'dwindling', 7252: 'benson', 12115: 'enroll', 501: 'revenues', 12116: 'impacted', 20826: 'queue', 10079: 'accomplished', 7602: 'throughput', 9396: 'influx', 10080: 'stockbuilding', 20827: 'aproximates', 13849: 'petroleo', 16335: 'sistemas', 14053: 'feretti', 5943: 'opposes', 882: 'working', 20829: 'perished', 13850: 'oldham', 20830: '27000', 19245: 'optimize', 20832: 'vigour', 1580: 'opposed', 16336: 'liberalizing', 20833: 'wvz', 20834: 'dampness', 13851: 'approving', 13496: 'sierra', 20835: 'entrepot', 224: 'currency', 1499: 'originally', 20837: 'tindemans', 16337: 'valorem', 477: 'following', 20838: 'fossen', 11016: 'locke', 20839: 'employess', 12117: 'rotberg', 16338: 'parachute', 11017: 'locks', 12255: 'incremental', 16339: 'woolowrth', 20841: 'listens', 7253: 'litre', 3554: 'edouard', 1377: 'ounce', 20843: 'nicanor', 20844: 'sucocitrico', 16340: 'minicomputers', 16341: "silva's", 11018: 'restitutions', 16342: 'custer', 2590: '3rd', 10081: 'fueled', 20845: 'trydahl', 11019: 'aice', 12118: 'harmon', 10082: 'conscious', 20846: 'herbicidesand', 20847: 'subdivisions', 20848: "veslefrikk's", 11020: 'swollen', 7978: 'pulled', 20849: 'tilney', 203: 'years', 20850: 'structuring', 20851: 'episodes', 16343: 'sportscene', 16344: "northair's", 20852: 'jig', 20853: 'jin', 3403: 'jim', 8359: 'troubles', 13852: 'workforces', 2362: 'suspension', 3892: 'troubled', 16345: 'fondiaria', 6697: 'modestly', 12119: 'recipients', 7979: 'civilian', 13853: 'indigenous', 20854: 'overpowering', 1051: 'drilling', 16346: 'sorted', 16347: 'lichtenstein', 20855: 'bedevil', 20856: 'dispite', 16843: 'battleships', 4824: 'instability', 95: 'quarter', 20857: 'salado', 5692: 'honduras', 13855: "chevron's", 12273: "lazere's", 2660: 'receipt', 8360: 'sponsor', 4825: 'entering', 16349: "kcbt's", 19987: 'nowicki', 13856: 'salads', 16351: 'augar', 7980: '797', 7254: '796', 8361: '795', 5295: '794', 5118: '793', 6170: '792', 5296: '791', 4826: '790', 20858: "nikko's", 20859: 'unsaleable', 5720: '799', 5693: '798', 2143: 'seriously', 16352: 'trauma', 20860: 'tvbh', 20861: 'macedon', 21906: 'disintegrated', 21909: 'adddition', 2244: 'incentives', 5944: 'complicated', 20864: 'reevaluating', 21921: 'thatching', 7981: 'brasil', 20865: '79p', 4951: 'wrong', 8822: 'initiate', 16353: 'aboard', 7255: 'saving', 8823: 'spoken', 16364: 'parkinson', 65: 'one', 20867: 'ont', 7256: 'concert', 16354: "boston's", 13859: 'stifled', 4622: 'types', 20868: 'lingering', 16356: 'surges', 20869: 'hurdman', 16357: 'herds', 14114: 'absorbs', 4681: 'surged', 14211: 'dalkon', 13860: 'crossroads', 20870: 'shakeup', 20871: 'disasterous', 11021: 'illness', 3242: 'turned', 3801: 'locations', 12120: 'tyranite', 13861: 'minesweepers', 7257: 'turner', 20872: 'borough', 12358: 'underlines', 20873: "bancorporation's", 20874: 'fashionable', 20875: "ae's", 16358: 'dilutions', 9472: 'goodman', 10510: 'unlawfully', 16359: 'mayer', 16360: 'printer', 20877: 'offload', 13862: 'opposite', 738: 'buffer', 9398: 'printed', 16361: 'pequiven', 13863: 'panoche', 20878: 'knowingly', 16362: 'ecusta', 20879: 'thsl', 8825: 'phil', 13864: 'jitters', 16363: 'touche', 20881: 'jittery', 3291: 'friction', 16365: 'fecal', 22068: 'resurgance', 20882: 'heeding', 2363: 'soviets', 16366: 'imagined', 16367: 'transact', 20883: 'califoirnia', 9399: "chrysler's", 16368: 'respecitvely', 16369: 'presse', 10084: 'euromarket', 12121: 'guarded', 16371: 'satisfacotry', 20884: 'authroization', 20885: 'simplistic', 20886: 'monde', 4102: 'awaiting', 13865: 'recombinant', 20887: 'refinancement', 20888: 'comserv', 20889: 'kitakyushu', 16372: 'pima', 11022: 'basle', 20891: '6250', 16373: 'choudhury', 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14016: 'kennecott', 21558: 'nuggets', 5960: 'assembly', 3084: 'sixth', 26260: 'assemble', 9107: 'exciting', 12223: 'veslefrikk', 21560: 'overrall', 21561: 'casuals', 21562: 'whbf', 21563: 'ideology', 14017: 'trailer', 21564: '7940', 18717: 'antibody', 9441: 'siddeley', 26276: 'mannheim', 1189: 'outlook', 3414: 'stabilisation', 8855: 'shrinking', 12224: 'mcgroarty', 12225: 'qualifying', 8856: 'hertz', 20828: 'suppositions', 621: 'start', 21566: "nordbanken's", 21567: 'politeness', 21568: 'afobaka', 6720: 'rjr', 21569: 'ealier', 16704: 'shipbreaking', 10146: 'smuggling', 1709: 'delayed', 22180: 'neccesarily', 16705: 'manipulative', 21570: 'recouped', 16370: 'pitched', 21572: 'becasue', 4960: 'fraud', 4560: 'default', 8391: 'kresge', 21573: 'besse', 16706: 'realigned', 21574: "as'", 4317: '605', 5961: '604', 4837: '607', 5131: '606', 5526: '601', 636: '600', 4022: '603', 3906: '602', 4961: '609', 4689: '608', 16707: 'leroux', 21575: 'moneyline', 21576: 'appraising', 12226: 'terminating', 4214: 'forcing', 1895: 'poor', 21577: '1350', 14018: 'ineligible', 21578: '60p', 4561: 'unocal', 21579: 'cazale', 21580: 'wallace', 21581: 'endeavors', 14019: 'dubuque', 21583: 'montly', 4562: 'queensland', 7288: 'pool', 11093: 'hambrecht', 1771: 'bulk', 16708: 'corel', 5315: 'roasters', 21584: 'corea', 3907: 'stockpile', 21585: "ross's", 1070: 'overseas', 16709: 'monte', 14020: 'ceases', 136: 'month', 8178: 'ceased', 12228: 'religious', 21587: 'broadbased', 6979: 'conger', 14021: 'surpised', 9442: 'wmx', 2510: 'chain', 2693: 'pledged', 8857: 'wyoming', 21588: 'insulate', 12229: 'lammers', 22474: 'locomotives', 8858: 'wmc', 21591: 'wmb', 5710: 'pledges', 14022: 'resounding', 12230: 'comercial', 21592: 'augusto', 21593: 'horror', 16711: 'witching', 21594: "hollinger's", 2023: 'decide', 21595: 'anat', 5962: 'targetted', 24155: '237p', 15248: 'promac', 14023: 'kondo', 21599: "o'sullivan", 10147: 'reaffirming', 26456: '385p', 21600: 'reformate', 14024: 'brucellosis', 10148: "darman's", 21601: 'streets', 10149: 'expansive', 3769: 'darman', 7998: 'enjoin', 11094: 'hamper', 11095: 'dunn', 4838: 'investing', 16713: 'ast', 21602: 'microbiology', 14026: 'paemen', 16714: 'bernhard', 8859: 'learned', 13857: 'ldbrinkman', 21603: 'richarson', 11096: 'tracks', 26499: 'acadia', 19468: 'halfway', 1606: 'excess', 26504: "petranol's", 14027: 'arena', 12231: 'conviction', 8860: 'geodyne', 505: 'losses', 620: 'base', 4839: 'advertising', 16717: 'nesbitt', 16718: 'successors', 21605: 'wallingford', 3679: 'requiring', 10150: 'steinhaeuser', 2121: 'ask', 14028: '700p', 21606: 'warplnes', 5132: 'conventional', 21607: 'heartened', 1411: 'oecd', 16719: 'oecf', 5711: 'mcivor', 4963: 'brunswick', 3561: 'bankamerica', 10151: 'pazzionotto', 14029: 'hannes', 16720: 'indenture', 21608: 'lazaro', 16721: 'garza', 21609: 'dior', 21610: '7001', 21611: '7000', 14030: 'lazard', 811: 'producing', 21612: 'antigone', 21613: 'grill', 7999: 'consortia', 21614: 'budgets', 12232: 'byers', 4023: '740', 4690: '741', 5133: '742', 6200: '743', 5527: '744', 5134: '745', 6721: '746', 4103: '747', 7289: '748', 6980: '749', 14031: 'phrase', 21615: 'publicise', 24161: 'cheering', 16722: 'mcdp', 21617: 'utilize', 26609: 'mongstad', 4318: 'reject', 17446: 'vigilance', 7371: '1970', 8861: 'schultz', 21619: 'koppabergs', 16723: 'communicating', 21620: 'winnebago', 9443: 'interfirst', 16724: 'solters', 21621: "jacobson's", 12233: 'compulsory', 21622: 'sesdaq', 21623: 'criticize', 21624: 'recnogized', 16937: 'mobilise', 10152: 'anytime', 22482: '401p', 21627: 'mangood', 23762: "spa's", 10153: 'thermal', 12234: "idc's", 21628: 'wishful', 21629: 'heron', 14032: 'bywater', 6512: 'territorial', 14033: 'mitek', 644: 'systems', 8393: 'differed', 16727: 'founders', 4691: "haven't", 21630: 'misalignment', 5963: 'evening', 16728: 'curtis', 16729: 'cea', 11097: 'pettee', 16730: 'ces', 26692: 'commodore', 16731: 'muskeg', 14035: 'mcadoo', 10154: 'burkhard', 11098: 'bryan', 16732: 'healthmate', 21631: "financiers'", 6722: 'liquefied', 21632: 'obligates', 21633: 'unmerciful', 16733: 'fairs', 14036: 'predators', 21634: 'radial', 11099: 'obligated', 21635: 'lifestyles', 9444: 'predatory', 425: 'barrels', 21636: 'exasperated', 21637: 'syacmore', 7290: 'punitive', 16734: 'jawa', 21638: 'anarchy', 21639: 'unsurpassed', 16735: 'testify', 16736: 'jolly', 3191: 'safety', 58: '7', 26732: 'jolla', 21641: "america'", 14037: 'schaik', 12235: "ariadne's", 21642: 'multnomah', 21643: 'freeboard', 11100: 'earns', 16737: 'housed', 6201: 'favored', 27678: 'foreland', 1671: 'houses', 21645: 'unsightly', 16738: 'brightest', 14038: 'concessionary', 21646: 'grovewood', 1205: 'speculation', 6202: 'unresolved', 14039: 'americas', 231: 'american', 11101: 'frenzel', 11102: 'yaik', 6723: 'barwon', 14040: 'morita', 16739: 'tf1', 10155: 'horse', 21649: 'dashwood', 11103: 'canbra', 2898: 'station', 9445: 'gibraltar', 6443: 'hundred', 5964: 'raws', 5135: 'trapped', 4692: 'ascs', 21651: '1912', 26799: 'asca', 16740: '1914', 8862: 'rawl', 1386: 'grew', 21653: 'gres', 21654: 'grex', 6981: 'grey', 21655: 'greb', 21656: "cooper's", 1682: 'toward', 10156: 'procedural', 5528: '883', 6444: '882', 5529: '881', 4964: '880', 6203: '887', 6204: '886', 5712: '885', 4840: '884', 5530: '889', 5136: '888', 8394: 'deflationary', 16741: 'committment', 21657: 'organs', 21658: 'associatioon', 21659: 'cherokee', 21660: "protection's", 16742: 'aired', 5336: 'redeemable', 21662: "worker's", 8863: 'hired', 16743: 'channeling', 4031: 'comparisons', 6982: 'soriano', 21664: 'channelink', 4841: 'privatised', 16744: 'bzw', 9902: 'levied', 26874: 'moonshine', 21665: 'confinement', 16745: 'outpatients', 6724: 'divestiture', 14041: 'tubes', 11104: 'jeopardy', 21666: 'celebrated', 8395: 'phenomenon', 8000: 'hovered', 9951: 'taxable', 21667: 'broadacasting', 6445: "nations'", 21668: 'leandro', 12236: 'zakum', 21669: 'distressing', 2430: 'justice', 11105: 'quel', 21670: 'criticising', 3640: 'rotterdam', 7438: 'grocery', 4842: 'feels', 3034: 'competing', 10157: 'combinations', 16747: "'silent", 21671: 'stopwork', 21672: 'pneumatics', 6983: 'adhering', 1255: 'retaliation', 12237: 'alsthom', 5714: 'stored', 21673: 'depresed', 21674: 'stn', 12238: 'lornex', 21675: 'hplg', 14042: 'becher', 12239: 'culture', 21676: "linde's", 21677: 'scandinavian', 3908: 'locals', 8001: 'pictures', 14043: 'tolls', 11106: 'stagnation', 8396: 'missing', 10158: 'ranked', 16749: 'dowty', 14044: 'abruptly', 8865: 'league', 5316: "wouldn't", 3562: 'minorities', 10159: 'canadians', 27001: 'experimental', 21678: 'hairdressing', 21679: 'servotronics', 12240: "edelman's", 12241: 'jewelmasters', 7721: 'deciding', 14762: 'waterside', 8002: 'subsidised', 16751: 'oamcaf', 16752: 'subsidises', 21681: 'tailgates', 4434: 'threaten', 16753: 'instructing', 8866: 'empty', 21682: 'tentiative', 5531: 'lived', 21683: 'billoin', 21684: 'propositions', 21685: 'sievers', 21686: 'amendemnt', 21687: 'haecke', 8867: 'lives', 12242: 'mombasa', 610: 'pact', 21688: 'derferred', 12219: 'warexin', 2472: '285', 8868: 'wyatt', 2398: 'pace', 16755: 'kara', 23302: 'adh', 14045: 'guido', 21690: 'rosecraft', 21691: 'paco', 9446: 'richter', 6446: 'guide', 9447: 'pack', 4693: 'costly', 21692: 'petal', 16756: 'kroger', 21693: 'dynavision', 12243: 'handelsblatt', 16757: 'payers', 11107: 'albany', 3132: 'grant', 21694: "cityquest's", 21695: 'albani', 4694: 'vulnerable', 4965: 'grand', 16758: 'pontins', 14046: 'composition', 7291: "turkey's", 9448: 'fatty', 5965: 'ratings', 12244: 'spedding', 7292: 'blair', 6984: '003', 5715: '002', 4843: '001', 15: '000', 6725: '007', 5966: '006', 5532: '005', 6726: '004', 6985: '009', 4966: '008', 6727: 'arturo', 21696: 'employement', 21697: 'poona', 4967: 'obviously', 21698: 'consistency', 21699: 'previoiusly', 2097: 'speculative', 16759: 'labatt', 21700: 'promedico', 8869: 'bearer', 14047: 'powerine', 5533: 'settlements', 4025: 'reviewed', 14048: '313p', 21701: 'mupawose', 21702: "michigan's", 21703: 'defintive', 12245: 'oporto', 2807: 'informal', 5317: 'nikko', 21704: 'robertshaw', 16760: "icco's", 16761: 'excavators', 4435: 'questioned', 21705: 'panamerica', 12246: 'rahim', 2468: 'showing', 14049: 'cgee', 5716: 'dubai', 21706: 'fssl', 2944: 'conable', 16762: 'dearly', 14050: 'cger', 21707: 'ponds', 21708: 'preopening', 1691: 'excluding', 21710: 'aabex', 14051: 'envisioned', 11108: 'stagnate', 2058: 'eep', 21711: 'buckhannon', 14052: 'eei', 4844: 'popular', 21712: 'softness', 16763: 'eec', 7629: 'lipc', 16764: 'caspian', 7977: 'petroles', 16765: 'conex', 165: 'economic', 14054: 'toufexis', 21714: 'impoverish', 27218: 'trotters', 14697: 'minebea', 11110: 'undergone', 21716: 'rht', 21717: 'viewpoints', 14056: 'negatives', 21718: 'postions', 16766: 'worgroce', 16767: 'bgl', 5318: 'papua', 21719: 'tananbaum', 10160: 'iomega', 8735: 'alpha', 21720: 'presupposes', 16768: 'purina', 12248: 'institut', 4436: 'bangladesh', 5534: 'placing', 21721: 'moseley', 5967: 'withholding', 21722: 'leduc', 21723: 'chimbote', 21724: 'v2500', 21725: 'edmin', 21726: 'mulberry', 4563: 'injection', 8292: 'seton', 16769: 'vacancies', 12249: 'munim', 1020: 'similar', 14059: 'nza', 2694: 'ordered', 21727: 'interventions', 5717: 'reopening', 16770: 'janatha', 14060: 'recalculated', 21729: 'ancestors', 1387: 'amounts', 14061: 'dashed', 1844: 'fears', 16771: 'feedlots', 1562: 'application', 9449: 'arabica', 21730: 'mondanto', 16772: 'haft', 210: 'department', 2730: 'manhattan', 21731: 'arithmetic', 16773: '5165', 21732: 'wohl', 8397: 'assayed', 13526: 'laroche', 8398: "firms'", 7829: "singapore's", 21734: 'svm', 1006: 'e', 16774: 'orbis', 6986: 'resolving', 14062: 'orbit', 12251: 'utilised', 14063: 'svz', 16775: 'bribe', 21735: 'imprecise', 5319: 'denman', 8399: 'compact', 21736: "eskey's", 21737: 'lkb', 1618: 'nippon', 10161: 'concessional', 21738: 'previoius', 14064: 'squaring', 3133: 'friendly', 16776: 'fieldcrest', 10162: 'miniere', 16777: 'rebounding', 1299: 'acreage', 3726: 'telling', 10163: 'sugarbeet', 21739: 'mauritania', 11111: 'csbc', 1479: 'positions', 21740: 'compassionate', 1845: 'michael', 14065: 'watered', 6987: 'ryan', 21741: 'csbp', 14066: 'geogia', 4800: 'certs', 8870: 'enforce', 9450: 'lazier', 21743: '8231', 12252: 'facilitating', 21744: "soybean's", 19206: 'auxton', 27459: 'okesman', 4215: 'jump', 3874: 'multilateral', 16778: 'tuobin', 12253: 'harmonious', 10164: 'brunei', 3909: 'becor', 14068: 'upsetting', 21746: 'madelin', 21747: 'japsper', 21748: 'mccrae', 6988: "cyacq's", 3727: 'convert', 21749: 'walkman', 21750: 'lyell', 16779: "nino's", 16780: 'genm', 21751: 'convern', 9451: 'gene', 16781: 'genb', 6728: 'patents', 9452: 'biopharmaceutics', 5535: 'foresee', 3246: 'clark', 11113: "manhattan's", 4564: 'manage', 12254: 'clara', 21752: 'revelving', 8400: 'buildup', 21753: 'expoloration', 21754: 'kraftwerk', 21755: '595p', 10165: 'camera', 7293: 'concepcion', 13645: 'asahi', 16782: 'depreciate', 8402: 'surveyed', 21756: 'isao', 16783: 'isam', 3563: 'formally', 4732: 'pretty', 21758: 'isab', 16784: 'visibility', 16785: 'ecop', 6729: 'warplanes', 21759: 'rivalry', 3247: 'appointed', 9453: 'keswick', 14069: 'stafford', 9454: 'lapse', 1628: 'averaged', 1710: 'recession', 20840: 'halldor', 16786: 'servants', 21760: 'reafforestation', 2788: '238', 21761: "dockers'", 8403: 'averages', 4105: 'withdrew', 6447: 'mees', 21762: 'ittihad', 4845: 'links', 22508: 'amtr', 1258: 'allowed', 11392: 'woodside', 16788: 'pulling', 1723: 'sought', 2123: 'dairy', 21764: 'incudes', 21765: 'defective', 2525: '230', 12256: 'narr', 21767: 'ordinaries', 21768: 'everly', 16789: 'sentiments', 21769: 'instinctively', 21770: 'filament', 14071: 'fare', 462: 'farm', 21771: 'faro', 21772: 'oilfileds', 21773: 'thunderstorm', 3415: 'ronald', 8404: "organization's", 21774: 'gebrueder', 14072: "fermenta's", 16790: 'indemnifying', 16791: 'vienot', 21775: 'surfrace', 11910: 'checkoff', 21776: 'alava', 345: 'including', 21777: 'acuqisition', 21778: "copy's", 9980: "adsteam's", 5137: 'temporao', 21779: "guaranty's", 21780: 'degadillo', 21781: 'articulation', 6205: 'uccel', 21782: 'eckhart', 16792: 'yugo', 16793: 'purusant', 12257: 'kilometres', 21783: 'overcharges', 3485: 'university', 11115: 'anzola', 2768: 'slide', 21784: 'firings', 10531: 'undertook', 3416: 'prevailing', 16795: 'shortfull', 21785: 'greyerz', 5968: 'constitute', 16796: 'investigative', 16797: 'separating', 16798:
# 출력되는 단어들을 공백을 줘서 연결(문장처럼 만들기)
print(' '.join([word_of_news[key] for key in X_train[0]]))the wattie nondiscriminatory mln loss for plc said at only ended said commonwealth could 1 traders now april 0 a after said from 1985 and from foreign 000 april 0 prices its account year a but in this mln home an states earlier and rise and revs vs 000 its 16 vs 000 a but 3 psbr oils several and shareholders and dividend vs 000 its all 4 vs 000 1 mln agreed largely april 0 are 2 states will billion total and against 000 pct dlrs
# .join 예시
print(' '.join(['오늘','점심','메뉴는','무엇이 좋을까요?']))오늘 점심 메뉴는 무엇이 좋을까요?
# 향상된 for문 원본 예시 (' '.join([word_of_news[key] for key in X_train[0]]))
train_len = []
for key in X_train[0]:
train_len.append(word_of_news[key])
print(' '.join(train_len))the wattie nondiscriminatory mln loss for plc said at only ended said commonwealth could 1 traders now april 0 a after said from 1985 and from foreign 000 april 0 prices its account year a but in this mln home an states earlier and rise and revs vs 000 its 16 vs 000 a but 3 psbr oils several and shareholders and dividend vs 000 its all 4 vs 000 1 mln agreed largely april 0 are 2 states will billion total and against 000 pct dlrs
# 뉴스의 주제 개수를 알아보자 - 46가지 종류의 주제가 존재함
np.unique(y_train)array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,
34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45])
CNN도 이미지 데이터를 모두 같은 크기로 변경시켰듯이 RNN도 뉴스기사의 단어 개수를 같이 맞춰줘야함 (RNN 신경망의 timesteps는 일정해야 하기 때문에)
긴 기사는 잘라내고, 짧은 기사는 붙여 넣어줘야함(padding)
# 전체 뉴스기사(8982개)의 기사별 단어 개수를 구해서 list에 담아줌
train_len = []
for news in X_train:
train_len.append(len(news))
# 향상된 for문
train_len = [len(news) for news in X_train]
print('최대값 : ', max(train_len))
print('최소값 : ', min(train_len))
print('평균값 : ', np.mean(train_len))
print('중앙값 : ', np.median(train_len))최대값 : 2376
최소값 : 13
평균값 : 145.5398574927633
중앙값 : 95.0
# 히스토그램으로 데이터의 밀도를 확인해보자!
# 가로축은 뉴스 길이를 각 구간별로 표시, 세로축은 뉴스기사의 개수
plt.hist(train_len, bins=20) # bins : 전체 길이에서 몇 개의 구간으로 나눌지 결정
plt.xlabel('news_len')
plt.ylabel('news_count')
plt.show()
# 라인차트로 각 기사별로 단어의 개수가 얼만큼 되는지 대략적으로 파악해보자!
plt.figure(figsize=(15,5))
plt.plot(train_len)
plt.xlabel('news_index')
plt.ylabel('word_len')
plt.show
문제 데이터 가공
전체 단어 개수의 분포를 고려해 가장 많은 개수를 차지하는 120개로 맞춰주자 (단어를 120개씩 설정해서 120번씩 순환시켜주자)
RNN 신경망은 같은 timesteps로 맞춰줘야하기 때문에 사전에 데이터의 길이를 같게 맞춰줘야함
즉, 뉴스 기사의 단어 길이를 120회로 순환 학습하여 어떤 주제의 기사인지를 분류하는 문제라고 볼 수 있음
# RNN신경망의 순환횟수(시퀀스)를 변경시켜주는 라이브러리
from tensorflow.keras.preprocessing.sequence import pad_sequences
# X_train, X_test에서 시퀀스를 120으로 설정
X_train_pad = pad_sequences(X_train, maxlen=120)
X_test_pad = pad_sequences(X_test, maxlen=120)
X_train_pad[0]
# RNN에서 padding을 진행하면 앞에서부터 0이 채워짐
# -> 뒤쪽에 0이 들어가면 후반부 연산에서 값이 제대로 전달되지 않을 수 있음
# 초반에는 신경망에 0 값이 들어가서 아무런 연산이 일어나지 않고 실제연산은 1부터 진햄array([ 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 27595, 28842,
8, 43, 10, 447, 5, 25, 207, 270, 5,
3095, 111, 16, 369, 186, 90, 67, 7, 89,
5, 19, 102, 6, 19, 124, 15, 90, 67,
84, 22, 482, 26, 7, 48, 4, 49, 8,
864, 39, 209, 154, 6, 151, 6, 83, 11,
15, 22, 155, 11, 15, 7, 48, 9, 4579,
1005, 504, 6, 258, 6, 272, 11, 15, 22,
134, 44, 11, 15, 16, 8, 197, 1245, 90,
67, 52, 29, 209, 30, 32, 132, 6, 109,
15, 17, 12], dtype=int32)
X_train_pad.shape, X_test_pad.shape((8982, 120), (2246, 120))
timesteps : hello 학습시에는 h,e,l,l 이라는 총 4개의 문자가 들어갔기 때문에 4였고, 현재는 각 뉴스기사의 단어 120개를 120번 순환시킬 것이기 때문에 120으로 설정
features : hello 학습시에는 원핫인코딩을 통해서 문제 데이터의 컬럼이 9개였기 때문에 9로 설정했고, 로이터뉴스 문제 데이터는 숫자 하나로만 표시(레이블인코딩) 되어져 있기 때문에 features는 1이 됨
'입력'은 문제데이터, '출력'은 정답데이터를 보고 생각하자!
X_train_pad_reshape = X_train_pad.reshape(8982, 120, 1)
X_test_pad_reshape = X_test_pad.reshape(2246, 120, 1)
X_train_pad_reshape.shape, X_test_pad_reshape.shape((8982, 120, 1), (2246, 120, 1))
RNN 신경망 모델링
from tensorflow.keras import Sequential
from tensorflow.keras.layers import Dense, SimpleRNN
model = Sequential()
model.add(SimpleRNN(500, input_shape=(120, 1)))
# 출력층
# 뉴스기사를 46개의 주제로 분류하는 문제이기 때문에 뉴런의 수는 46개, 활성화함수는 softmax(다중분류)
model.add(Dense(46, activation='softmax'))
model.compile(loss='sparse_categorical_crossentropy',
optimizer='Adam',
metrics=['acc']
)
h = model.fit(X_train_pad_reshape, y_train,
validation_split=0.2,
epochs=50,
batch_size=128
)
plt.figure(figsize=(15,5))
# train 데이터
plt.plot(h.history['acc'],
label='acc',
c = 'blue',
marker='.'
)
# val 데이터
plt.plot(h.history['val_acc'],
label='val_acc',
c = 'red',
marker='.'
)
plt.xlabel("epochs")
plt.ylabel("accuracy")
plt.legend()
plt.show()
SimpleRNN만 가지고는 수십수백개의 단어들로 이루어진 복잡한 데이터를 순서까지 고려해서 다 분류해내기 어려움
LSTM과 Word Embedding을 적용
SimpleRNN과는 달리 이전의 중요 데이터들을 모두 기억하여 예측에 반영
LSTM에 덧붙여 뉴스기사의 단어들을 다양하게 표현할 수 있도록 Embedding도 활용
from tensorflow.keras.layers import LSTM, Embedding
model1 = Sequential()
# input_dim : 데이터 내에서 사용할 최대 단어의 개수(뉴스기사 대부분은 1000개 이하의 단어로 구성)
# output_dim : 임베딩 층을 통과한 후 생성된 실수의 개수(단어를 얼마만큼 다양하게 표현해줄수 있느냐)
model1.add(Embedding(input_dim=1000, output_dim=100))
model1.add(LSTM(1000))
model1.add(Dense(46, activation='softmax'))
model1.compile(loss='sparse_categorical_crossentropy',
optimizer='Adam',
metrics=['acc']
)
h1 = model1.fit(X_train_pad_reshape, y_train,
validation_split=0.2,
epochs=50,
batch_size=128
)
plt.figure(figsize=(15,5))
# train 데이터
plt.plot(h1.history['acc'],
label='acc',
c = 'blue',
marker='.'
)
# val 데이터
plt.plot(h1.history['val_acc'],
label='val_acc',
c = 'red',
marker='.'
)
plt.xlabel("epochs")
plt.ylabel("accuracy")
plt.legend()
plt.show()
model1.evaluate(X_test_pad_reshape, y_test)71/71 [==============================] - 1s 20ms/step - loss: 1.3989 - acc: 0.6986
[1.3989092111587524, 0.6985752582550049]
