Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
城ヶ崎美嘉で学ぶRNNLM
Search
Kento Nozawa
June 05, 2016
Programming
2
3k
城ヶ崎美嘉で学ぶRNNLM
オタク機械学習勉強会#0 のLT
Kento Nozawa
June 05, 2016
Tweet
Share
More Decks by Kento Nozawa
See All by Kento Nozawa
Analysis on Negative Sample Size in Contrastive Unsupervised Representation Learning
nzw0301
0
180
[IJCAI-ECAI 2022] Evaluation Methods for Representation Learning: A Survey
nzw0301
0
650
[NeurIPS Japan meetup 2021 talk] Understanding Negative Samples in Instance Discriminative Self-supervised Representation Learning
nzw0301
0
230
[IBIS2021] 対照的自己教師付き表現学習おける負例数の解析
nzw0301
0
210
Understanding Negative Samples in Instance Discriminative Self-supervised Representation Learning
nzw0301
0
520
Introduction of PAC-Bayes and its Application for Contrastive Unsupervised Representation Learning
nzw0301
2
850
NLP Tutorial; word representation learning
nzw0301
0
230
Analyzing Centralities of Embedded Nodes
nzw0301
0
200
Paper Reading: Noise-Contrastive Estimation of Unnormalized Statistical Models, with Applications to Natural Image Statistics
nzw0301
2
1.2k
Other Decks in Programming
See All in Programming
AIで開発はどれくらい加速したのか?AIエージェントによるコード生成を、現場の評価と研究開発の評価の両面からdeep diveしてみる
daisuketakeda
1
2.5k
React 19でつくる「気持ちいいUI」- 楽観的UIのすすめ
himorishige
11
7.5k
AIフル活用時代だからこそ学んでおきたい働き方の心得
shinoyu
0
140
OCaml 5でモダンな並列プログラミングを Enjoyしよう!
haochenx
0
140
フロントエンド開発の勘所 -複数事業を経験して見えた判断軸の違い-
heimusu
7
2.8k
Apache Iceberg V3 and migration to V3
tomtanaka
0
170
CSC307 Lecture 08
javiergs
PRO
0
670
The Past, Present, and Future of Enterprise Java
ivargrimstad
0
620
組織で育むオブザーバビリティ
ryota_hnk
0
180
コマンドとリード間の連携に対する脅威分析フレームワーク
pandayumi
1
460
日本だけで解禁されているアプリ起動の方法
ryunakayama
0
240
16年目のピクシブ百科事典を支える最新の技術基盤 / The Modern Tech Stack Powering Pixiv Encyclopedia in its 16th Year
ahuglajbclajep
5
1k
Featured
See All Featured
Leading Effective Engineering Teams in the AI Era
addyosmani
9
1.6k
How to Ace a Technical Interview
jacobian
281
24k
Efficient Content Optimization with Google Search Console & Apps Script
katarinadahlin
PRO
1
330
AI Search: Implications for SEO and How to Move Forward - #ShenzhenSEOConference
aleyda
1
1.1k
Digital Projects Gone Horribly Wrong (And the UX Pros Who Still Save the Day) - Dean Schuster
uxyall
0
390
How Fast Is Fast Enough? [PerfNow 2025]
tammyeverts
3
460
Sam Torres - BigQuery for SEOs
techseoconnect
PRO
0
190
Responsive Adventures: Dirty Tricks From The Dark Corners of Front-End
smashingmag
254
22k
Building Experiences: Design Systems, User Experience, and Full Site Editing
marktimemedia
0
410
Applied NLP in the Age of Generative AI
inesmontani
PRO
4
2.1k
[RailsConf 2023 Opening Keynote] The Magic of Rails
eileencodes
31
9.9k
Statistics for Hackers
jakevdp
799
230k
Transcript
ϲ࡚ඒՅ Λը૾ݕࡧ͓ͯͪ͠Լ͍͞
ϲ࡚ඒՅͰֶͿ RNNLM 2016/6/5 ΦλΫػցֶशษڧձ #0 @nzw0301
Ϟνϕʔγϣϯ ϲ࡚ඒՅͷηϦϑੜ
Recurrent Neural Network Language Model • ηϦϑੜ: લ·Ͱͷ୯ޠ͔Β࣍ͷ1୯ޠΛ༧ଌ͠ଓ͚Δ • ྫɿΊΔΊΔʜᣦՅʹϝʔϧૹ৴ͬ˒
• ୯ޠׂ: <BOS> ΊΔΊΔʜᣦՅʹϝʔϧૹ৴ͬ˒&04 • ֶश: Q ΊΔΊΔc#04 ͱ͔ Q ᣦՅc<BOS>, ΊΔΊΔ ʜ
RNNLMͷߏ ޠኮV࣍ݩͷϕΫτϧ softmax ؔ 1ͭલͷதؒͷϕΫτϧ RNNͷ༝ԑ h࣍ݩͷதؒ
p(ΊΔΊΔ|<BOS>) ͷܭࢉྫɿೖྗ w #04ͷPOFPG,දݱΛೖྗ w ࣍ݩͰີͳϕΫτϧʹม <BOS> ΊΔΊΔ 0 B
B B B B @ 0 1 0 . . . 0 1 C C C C C A
p(ΊΔΊΔ|<BOS>) ͷܭࢉྫɿதؒ • ີͳϕΫτϧΛதؒʹ͢ • ଟύʔηϓτϩϯͱಉ͡ <BOS> ΊΔΊΔ
p(ΊΔΊΔ|<BOS>) ͷܭࢉྫɿग़ྗ • ग़ྗʹதؒͷϕΫτϧΛ͢ • ݱࡏͷதؒͷΛอ࣋ <BOS> ΊΔΊΔ
p(ΊΔΊΔ|<BOS>) ͷܭࢉྫɿॏΈߋ৽ • SoftmaxؔͰ֬Λܭࢉ • Backpropagation Ͱ ΊΔΊΔ ͷ͕֬େ͖͘ͳΔΑ͏ʹߋ৽ <BOS>
ΊΔΊΔ
p(ʜc#04 ΊΔΊΔ) ͷܭࢉྫɿೖྗ ૄΊΔΊΔϕΫτϧΛೖྗ͠ɼີͳΊΔΊΔϕΫτϧʹม p(ΊΔΊΔ|<BOS>)Ͱܭࢉͨ͠தؒͷϕΫτϧ ʜ ΊΔΊΔ 0 B B
B B B B B B B B @ 0 . . . 0 1 0 . . . 0 1 C C C C C C C C C C A
p(ʜc#04 ΊΔΊΔ) ͷܭࢉྫɿதؒ ີͳΊΔΊΔϕΫτϧͱલʹܭࢉͨ͠தؒͷϕΫτϧΛதؒ p(ΊΔΊΔ|<BOS>)Ͱܭࢉͨ͠தؒͷϕΫτϧ ʜ ΊΔΊΔ
p(ʜc#04 ΊΔΊΔ) ͷܭࢉྫɿग़ྗ • ग़ྗʹதؒͷϕΫτϧΛͯ͠ɼݱࡏͷதؒͷϕΫτϧΛอ࣋ p(ʜ|<BOS>, ΊΔΊΔ)Ͱܭࢉͨ͠தؒͷϕΫτϧ ʜ ΊΔΊΔ
p(ʜc#04 ΊΔΊΔ) ͷܭࢉྫɿॏΈߋ৽ • SoftmaxؔͰ֬Λܭࢉ • Backpropagation Ͱ ʜ ͷ͕֬େ͖͘ͳΔΑ͏ʹߋ৽
ʜ ΊΔΊΔ
࣮ݧ
࣮ݧ֓ཁ • SCRNΛ༻ • LSTM GRU ΛΘͳ͍ • Keras
Ͱ࣮ • લॲཧ • ܗଶૉղੳͤͣʹจࣈ୯ҐͰֶश • /。|★|?|!|♪/ ͰηϦϑΛׂ • 900ηϦϑ (Վࢺ) Λ༻ • ϞόϚε • σϨες • TOKIMEKIΤεΧϨʔτ
݁Ռ
10epochޙɿϓϩσϡʔαʔͷҰ෦͕ͱΕͯΔ ϓϩσϩσϡʔͯͳͪʙʹෲΞλ γ΄ϡʔαʔΒతͳʔɺͨ͜ͳ
40epochޙɿΪϟϧޠʁ ϓϩσϡʔαʔʹ͍ͪΌΜɺ ݟ͘ͳ͍ʔ͘ͱԿߴͩ͠ʔͬ̇
80epochޙɿݺΕͨؾ͕ͨ͠ ϓϩσϡʔαʔ!
“<BOS> ϓ” ͔Β࠷ਪఆɿϧʔϓ ϓϩσϡʔαʔɺΞλγͷ͜ͱ͔Βɺ ϓϩσϡʔαʔɺΞλγͷ͜ͱ
ϥϯμϜʹηϦϑੜ
ॴײ • ηϦϑΛͲ͜ͰΔ͖͔ • ྫɿ͝Μʹ͢Δ?͓෩࿊ʹ͢Δ?…͜ΕͪΐͬͱϕλͬΆ͍ͳ͊ • ? Ͱ۠Δ͖͔൱͔ • …લޙͲͬͪͰ۠Δ͔൱͔ʁͦΕͱͳ͘͢ʁ
• ήʔϜը໘ͷͨΊ͔1ηϦϑܥྻ͕΄΅Ұఆʢֶͼʣ
ࢀߟจݙͳͲ • http://keras.io/ • DLͷϥΠϒϥϦ • ָ͍͢͝ʹॻ͚Δ • Mikolov at.el.
Recurrent neural network based language model. 2010. • RNNͷը૾͜ͷจͷͷΛ༻ • Mikolov at.el Learning Longer Memory in Recurrent Neural Networks. 2014. • ࠓճ༻ͨ͠Ϟσϧ