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Evaluate adding LSTM network to Encog #165

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jeffheaton opened this issue Jan 16, 2014 · 12 comments
Closed

Evaluate adding LSTM network to Encog #165

jeffheaton opened this issue Jan 16, 2014 · 12 comments

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@jeffheaton
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There have been several requests to add a LSTM network to Encog. Some discussion of it here. http://www.heatonresearch.com/comment/1231#comment-1231

Wikipedia Entry: http://en.wikipedia.org/wiki/Long_short_term_memory
More formal description: ftp://ftp.idsia.ch/pub/juergen/lstm.pdf

At this point I am unfamiliar with this architecture, so I am adding this issue to track it. Any suggestions/comments are welcome.

Initial thoughts... could this be implemented with the freeform networks. Or would it be better to create a new MLMethod.

@wilbown
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wilbown commented Jan 20, 2014

+1 to this

@szymanowski
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@SeverinAlexB
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+1 too

@cognivator
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another +1

I've been meaning to take a stab at extending Encog with LSTM for some time, now.

@Joshuaalbert
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I'm working on this also. Any collaborators willing to bandy ideas about?

UPDATE: I have it working with encog using freeform networks. I'm using blocks of training rates as per http://arxiv.org/pdf/1206.1106.pdf for the different gates. Anyone interested can contact me :)

Josh

@Turakar
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Turakar commented May 20, 2016

@Joshuaalbert

What's the status of your work? Can you publish it in a repository?

@Joshuaalbert
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Joshuaalbert commented Jun 20, 2016

@Turakar It is nearly working. Working on a temporal connectionist training for it, to allow unaligned pattern training.

@automenta
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our latest version is here, it's pure java (not involving any encog). it has an experimental continuous / online learning mode

https://github.com/automenta/narchy/tree/skynet1/logic/src/main/java/nars/learn/lstm

it would be nice to add the new Grid LSTM also

@zjuhasz
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zjuhasz commented Sep 13, 2017

Any updates on this? I'd really like to see it added.

@automenta
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i dont have any updates with any LSTM code. but here's a new variation that might be good too:
https://arxiv.org/abs/1709.02755

@jeffheaton
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I am going to close at this point, as I am not actively looking at adding every possible algorithm to Encog. At this point I am mainly adding ones that I have need of and are not well represented elsewhere. Also, it sounds like LSTM's future may be uncertain, based on several sources, but summarized nicely here: https://towardsdatascience.com/the-fall-of-rnn-lstm-2d1594c74ce0

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