[B! RNN] tsu-neraã®ãƒ–ãƒƒã‚¯ãƒžãƒ¼ã‚¯

tsu-nera id:tsu-nera

RNNã«é–¢ã™ã‚‹tsu-neraã®ãƒ–ãƒƒã‚¯ãƒžãƒ¼ã‚¯ (13)

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äººå·¥çŸ¥èƒ½ æŠ€è¡“ï¼ˆRecurrent Neural Networkã®1ã¤ã§ã‚ã‚‹GRUï¼‰ã‚’ä½¿ã£ã¦ã€ãƒ©ã‚¤ãƒ³ãƒˆãƒ¬ãƒ¼ã‚¹ãƒãƒœãƒƒãƒˆã®ãŸã‚ã®åˆ¶å¾¡ãƒ—ãƒã‚°ãƒ©ãƒ ã‚’ä½œã£ã¦ã¿ã¾ã—ãŸã€‚è¨“ç·´ã‚’ç¹°ã‚Šè¿”ã™ã“ã¨ã«ã‚ˆã‚Šã€æ»‘ã‚‰ã‹ãªãƒ©ã‚¤ãƒ³ãƒˆãƒ¬ãƒ¼ã‚¹ã‚’ã§ãã‚‹ã‚ˆã†ã«ãªã‚Šã¾ã™ã€‚- ãƒ—ãƒã‚°ãƒ©ãƒ ã®å…¬é–‹å¦ç¿’ã®ãŸã‚ã®ãƒ—ãƒã‚°ãƒ©ãƒ ã¨ã‚·ãƒŸãƒ¥ãƒ¬ãƒ¼ã‚¿ã‚’å…¬é–‹äºˆå®šã§ã™(ç¾åœ¨ã€æº–å‚™ä¸ã§ã™)ã€‚LEGO Mindstorms ã®åˆ¶å¾¡ãƒ—ãƒã‚°ãƒ©ãƒ ã®æ–¹ã¯ETãƒãƒœã‚³ãƒ³ã®ãƒŽã‚¦ãƒã‚¦ãŒå«ã¾ã‚Œã¦ã„ã‚‹ã®ã§éžå…¬é–‹ã§ã™ã€‚- å‰å›žå‹•ç”»DQNï¼ˆã‚³ãƒ³ãƒ”ãƒ¥ãƒ¼ã‚¿ï¼‰ã§CODE VS 5.0ã«æŒ‘æˆ¦ : mylist/56938120å‰ç·¨ã‚’ nuæ§˜ ã«å®£ä¼ã—ã¦ã„ãŸã ãã¾ã—ãŸã€‚ã‚ã‚ŠãŒã¨ã†ã”ã–ã„ã¾ã—ãŸã€‚
tsu-nera 2017/06/09
RNN

ETãƒãƒœã‚³ãƒ³

deep learning
ãƒªãƒ³ã‚¯
Tfå‹‰å¼·ä¼š(5)
2. 2 2016/9/27 Singularity Copyright 2016 Singularity Inc. All rights reserved ïƒ˜ è‡ªå·±ç´¹ä»‹ æ–°æ‘æ‹“ä¹Ÿ - ã‚·ãƒ¼ã‚¨ã‚¤ãƒˆãƒ©ãƒœæ ªå¼ä¼šç¤¾ ä»£è¡¨å–ç· å½¹ - ã‚·ãƒ³ã‚®ãƒ¥ãƒ©ãƒªãƒ†ã‚£æ ªå¼ä¼šç¤¾ å–ç· å½¹CTO - æ©Ÿæ¢°å¦ç¿’ã®ãŸã‚ã®æ•°å¦å¡¾ - RNN camp 3. 3 RNN campã«ã¤ã„ã¦ 2016/9/27 Singularity Copyright 2016 Singularity Inc. All rights reserved - æµ…å·ä¼¸ä¸€å…ˆç”Ÿã«ã‚ˆã‚‹RNNã«ç‰¹åŒ–ã—ãŸãƒãƒ³ ã‚ºã‚ªãƒ³å½¢å¼ã®å‹‰å¼·ä¼š - ç¾åœ¨ç¬¬äºŒå›ž - http://connpass.com/event/37205/ 4. 4 æ¦‚è¦ ïµ ä½•ã‚„ã‚‰æœ€è¿‘æ¡ˆä»¶ã§RNNã‚’ä½¿ã†ã“ã¨ãŒã€ã€ ïµ RNN campã‚‚ã‚ã£ã¦ã€è‡ªåˆ†ãªã‚Šã«è‰²ã€…ã¨RNNã®TensorFlowå®Ÿè£…ã«ã¤ ã„ã¦è©¦
tsu-nera 2017/06/03
tensorflow

rnn
ãƒªãƒ³ã‚¯
Some music generated by char-rnn
tsu-nera 2017/05/19
char-rnn

rnn
ãƒªãƒ³ã‚¯
ãƒªã‚«ãƒ¬ãƒ³ãƒˆãƒ‹ãƒ¥ãƒ¼ãƒ©ãƒ«ãƒãƒƒãƒˆãƒ¯ãƒ¼ã‚¯ã®ç†ä¸å°½ãªåŠ¹åŠ›ï¼ˆç¿»è¨³ï¼‰ - Qiita
ä¸Šè¨˜ã¯ã€æ™®é€šã® RNN ã®å‰é€²ãƒ‘ã‚¹ã®ä»•æ§˜ã‚’æ±ºã‚ã¾ã™ã€‚ãã®ãŸã‚ã® RNN ã®ãƒ‘ãƒ©ãƒ¡ãƒ¼ã‚¿ã¯ã€ï¼“ã¤ã®è¡Œåˆ— $W_{hh}, W_{xh}, W_{hy}$ ã§ã™ã€‚éš ã‚ŒçŠ¶æ…‹ $self.h$ ã¯ã€ã‚¼ãƒãƒ™ã‚¯ãƒˆãƒ«ã§åˆæœŸåŒ–ã•ã‚Œã¾ã™ã€‚$np.tanh$ é–¢æ•°ã¯ã‚¢ã‚¯ãƒ†ã‚£ãƒ™ãƒ¼ã‚·ãƒ§ãƒ³ã‚’ $[-1, 1]$ ã®ç¯„å›²ã«æŠ¼ã—ã¤ã¶ã™ã€éžç·šå½¢æ€§ã‚’å®Ÿç¾ã—ã¾ã™ã€‚ã©ã®ã‚ˆã†ã«ä½œç”¨ã™ã‚‹ã®ã‹ç°¡å˜ã«è¿°ã¹ã‚‹ã¨ï¼štanh å†…ã«ï¼’ã¤ã®é …ãŒã‚ã‚Šã¾ã™ï¼šä¸€æ–¹ã¯å‰ã®éš ã‚ŒçŠ¶æ…‹ã«åŸºã¥ãã€ä»–æ–¹ã¯ç¾åœ¨ã®å…¥åŠ›ã«åŸºã¥ãã¾ã™ã€‚numpy ã® $np.dot$ ã¯è¡Œåˆ—ã®æŽ›ã‘ç®—ã§ã™ã€‚ï¼’ã¤ã®ä¸é–“é …ã¯åŠ ç®—ã•ã‚Œã€tanh ã«ã‚ˆã‚Šæ–°ãŸãªçŠ¶æ…‹ãƒ™ã‚¯ãƒˆãƒ«ã«æŠ¼ã—ã¤ã¶ã•ã‚Œã¾ã™ã€‚æ•°å¦ã®è¡¨è¨˜ã«æ…£ã‚Œã¦ã„ã‚‹ãªã‚‰ã€éš ã‚ŒçŠ¶æ…‹ã®æ›´æ–°ã¯ä»¥ä¸‹ã®ã‚ˆã†ã«è¨˜è¿°å¯èƒ½ã§ã™ã€‚$h_t = \tanh ( W_{hh} h_{t-1} + W_{xh} x_t )$ ã€ã“ã“ã§ tanh ã¯è¦ç´ ã”ã¨ã«é©ç”¨ã•ã‚Œã¾ã™ã€‚
tsu-nera 2017/05/19
rnn

lstm
ãƒªãƒ³ã‚¯
Wise.io â€“ Machine Learning for the Industrial Internet of Things
Machine Learning for the Industrial Internet of Things Minds+Machines is the premier Industrial Internet event featuring keynotes from industry luminaries, insightful forums, and hands-on demos. Wise.io hosted itsÂ first Industrial Machine Learning Workshop (IMLW17) on October 24, 2017. View our presenters. Our flagship product, Wise Support, is now part of AnswerIQ! Wise.io is a well-resourced, r
tsu-nera 2017/05/19
rnn

deep learning

lstm
ãƒªãƒ³ã‚¯
HugeDomains.com
Captcha security check yoavz.com is for sale Please prove you're not a robot View Price Processing
tsu-nera 2017/05/18
LSTM

Deep Learning

RNN
ãƒªãƒ³ã‚¯
â€œLislâ€™s Stisâ€: Recurrent Neural Networks for Folk Music Generation
tsu-nera 2017/05/18
RNN

Deep Learning
ãƒªãƒ³ã‚¯
Training a Recurrent Neural Network to Compose Music | maraoz
Over the past weeks Iâ€™ve been slowly learning about recent developments in Machine Learning, specifically Neural Networks. Iâ€™ve seen really mind-blowing examples of the power of such architectures, from recreating images using particular art styles to automatically forming word representations that account for pretty high-level semantic relations. Recurrent Neural Networks (their most frecuent for
tsu-nera 2017/05/18
RNN

abc notation
ãƒªãƒ³ã‚¯
Composing Music With Recurrent Neural Networks
(Update: A paper based on this work has been accepted at EvoMusArt 2017! See here for more details.) It's hard not to be blown away by the surprising power of neural networks these days. With enough training, so called "deep neural networks", with many nodes and hidden layers, can do impressively well on modeling and predicting all kinds of data. (If you don't know what I'm talking about, I recomm
tsu-nera 2017/05/18
Deep Learning

RNN
ãƒªãƒ³ã‚¯
ãƒ‡ã‚£ãƒ¼ãƒ—ãƒ©ãƒ¼ãƒ‹ãƒ³ã‚°(seq2seq)ã§twitter chatbotã‚’ä½œã£ã¦ã¿ãŸ - Qiita
ã“ã‚“ã«ã¡ã¯ã€‚ä»Šå¹´ã¯å†¬ä¼‘ã¿ã‚’ã¨ã¦ã‚‚é•·ãã¨ã£ãŸã®ã§ã™ãŒã€è‚‰ã‚„èŸ¹ã‚„é¤…ã‚„é…’ã‚’é€£æ—¥æ¶ˆè²»ã—ã¦ã„ã‚‹ã†ã¡ã«äººä½“ãŒçµ‚ã‚ã£ã¦ã„ãã€æ°—æŒã¡ã«ãªã£ãŸã®ã§(æ§˜ã€…ãªæ–¹å‘ã«æ„Ÿæ¥µã¾ã£ãŸçŠ¶æ…‹ã®ã“ã¨ã‚’ã€Œæ°—æŒã¡ã«ãªã‚‹ã€ã¨è¡¨ã—ã¾ã™)ã€ä¸–é–“ã§æµè¡Œã£ã¦ã„ã‚‹ã‚‰ã—ã„ãƒ‡ã‚£ãƒ¼ãƒ—ãƒ©ãƒ¼ãƒ‹ãƒ³ã‚°ã®é–¢é€£æƒ…å ±ã‚’ã¤ã¾ã¿é£Ÿã„ã—ã¦ãƒãƒ£ãƒƒãƒˆãƒœãƒƒãƒˆã‚’ä½œã£ã¦ã¿ã‚‹ã“ã¨ã«ã—ã¾ã—ãŸã€‚ å…¥åŠ›æ–‡ã«å¯¾ã—ãƒ‹ãƒ¥ãƒ¼ãƒ©ãƒ«ãƒãƒƒãƒˆ(RNN)ã§å¿œç”æ–‡ã‚’ç”Ÿæˆã—ã¦è¿”äº‹ã—ã¾ã™ã€‚ @neural_chatbot ã¨ã„ã†twitterã‚¢ã‚«ã‚¦ãƒ³ãƒˆã§å‹•ã‹ã—ã¦ã„ã¾ã™ã€‚ ã”èˆˆå‘³ãŒã‚ã‚Œã°ãœã²@neural_chatbotã«è©±ã—ã‹ã‘ã¦ã¿ã¦ãã ã•ã„ã€‚ ã‚ã‚‰ã™ã˜ ãƒ‹ãƒ¥ãƒ¼ãƒ©ãƒ«ãƒãƒƒãƒˆã¨ã„ã†ã‚‚ã®ãŒã‚ã‚Šã€é–¢æ•°ã‚’è¿‘ä¼¼ã™ã‚‹ã“ã¨ãŒã§ãã€çŸ¥ã‚‰ã‚Œã¦ã„ã¾ã™ã€‚ Recurrent Neural Network (RNN)ã¨ã„ã†ã‚‚ã®ãŒã‚ã‚Šã€å†…éƒ¨çŠ¶æ…‹ã‚’æŒã¤ã“ã¨ãŒã§ãã€è‡ªç„¶è¨€èªžã‚’å«ã‚€å¯å¤‰é•·ã®ç³»åˆ—ã‚’å–ã‚Šæ‰±ã†ã®ã«ä¾¿åˆ©ã§ã€çŸ¥ã‚‰ã‚Œã¦ã„ã¾ã™ã€‚ Sequ
tsu-nera 2017/05/13
RNN

chatbot

seq2seq
ãƒªãƒ³ã‚¯
Deeplearning 4 j ã®ã‚¯ã‚¤ãƒƒã‚¯ã‚¹ã‚¿ãƒ¼ãƒˆã‚¬ã‚¤ãƒ‰ - Deeplearning4j: Open-source, Distributed Deep Learning for the JVM
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tsu-nera 2017/05/05
RNN

LSTM

deep learning
ãƒªãƒ³ã‚¯
LSTMãƒãƒƒãƒˆãƒ¯ãƒ¼ã‚¯ã®æ¦‚è¦ - Qiita
Deleted articles cannot be recovered. Draft of this article would be also deleted. Are you sure you want to delete this article? Christopher Olahæ°ã®ãƒ–ãƒã‚°è¨˜äº‹ http://colah.github.io/posts/2015-08-Understanding-LSTMs/ ã®ç¿»è¨³ã§ã™ã€‚ ç¿»è¨³ã®èª¤ã‚Šãªã©ã‚ã‚Œã°ã”æŒ‡æ‘˜ãŠå¾…ã¡ã—ã¦ãŠã‚Šã¾ã™ã€‚ ##ãƒªã‚«ãƒ¬ãƒ³ãƒˆãƒ‹ãƒ¥ãƒ¼ãƒ©ãƒ«ãƒãƒƒãƒˆãƒ¯ãƒ¼ã‚¯ äººé–“ã¯æ¯Žç§’ã‚¼ãƒã‹ã‚‰æ€è€ƒã‚’é–‹å§‹ã™ã‚‹ã“ã¨ã¯ã‚ã‚Šã¾ã›ã‚“ã€‚ã“ã®ã‚¨ãƒƒã‚»ã‚¤ã‚’èªã‚“ã§ã„ã‚‹é–“ã€ã‚ãªãŸã¯å‰ã®å˜èªžã®ç†è§£ã«åŸºã¥ã„ã¦ã€å„å˜èªžã‚’ç†è§£ã—ã¾ã™ã€‚ã™ã¹ã¦ã‚’æ¨ã¦ã¦ã€ã¾ãŸã‚¼ãƒã‹ã‚‰æ€è€ƒã‚’é–‹å§‹ã—ã¦ã¯ã„ã¾ã›ã‚“ã€‚ã‚ãªãŸã®æ€è€ƒã¯æŒç¶šæ€§ã‚’æŒã£ã¦ã„ã¾ã™ã€‚ å¾“æ¥ã®ãƒ‹ãƒ¥ãƒ¼ãƒ©ãƒ«ãƒãƒƒãƒˆãƒ¯ãƒ¼ã‚¯ã¯ã€ã“ã‚Œã‚’è¡Œã†ã“ã¨ãŒã§ãã¾
tsu-nera 2017/05/04
lstm

RNN

deep learning
ãƒªãƒ³ã‚¯
Understanding LSTM Networks -- colah's blog
Recurrent Neural Networks Humans donâ€™t start their thinking from scratch every second. As you read this essay, you understand each word based on your understanding of previous words. You donâ€™t throw everything away and start thinking from scratch again. Your thoughts have persistence. Traditional neural networks canâ€™t do this, and it seems like a major shortcoming. For example, imagine you want to
tsu-nera 2017/05/04
deep learning

lstm

RNN
ãƒªãƒ³ã‚¯
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