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57 lines
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57 lines
2.3 KiB
Markdown
[#]: collector: (lujun9972)
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[#]: translator: ( )
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[#]: reviewer: ( )
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[#]: publisher: ( )
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[#]: url: ( )
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[#]: subject: (Day 10: Training an RNN to count to three)
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[#]: via: (https://jvns.ca/blog/2020/11/20/day-10--training-an-rnn-to-count-to-three/)
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[#]: author: (Julia Evans https://jvns.ca/)
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Day 10: Training an RNN to count to three
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======
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Yesterday I was trying to train an RNN to generate English that sounds kind of like Shakespeare. That was not working, so today I instead tried to do something MUCH simpler: train an RNN to generate sequences like
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```
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0 1 2 0 1 2 0 1 2 0 1 2
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```
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and slightly more complicated sequences like
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```
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0 1 2 1 0 1 2 1 0 1 2 1 0 1 2 1 0
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```
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I used (I think) the exact same RNN that I couldn’t get to work yesterday to generate English by training it on Shakespeare, so it was cool to see that I could at least use it for this much simpler task (memorize short sequences of numbers).
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### the jupyter notebook
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It’s late so I won’t explain all the code in this blog post, but here’s the PyTorch code I wrote to train the RNN to count to three.
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* Here it is as a [github gist][1]
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* and [here it is on Colab][2] if you want to run it yourself
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In the gist there are a few experiments with different sequence lengths, like (unsurprisingly) it takes longer to train it to memorize a sequence of length 20 than a sequence of length 5.
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### simplifying is nice
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I’m super happy that I got an RNN to do something that I actually understand! I feel pretty hopeful that on Monday I’ll be able to go back to the character RNN problem of trying to get the RNN to generate English words now that I have this simpler thing working.
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--------------------------------------------------------------------------------
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via: https://jvns.ca/blog/2020/11/20/day-10--training-an-rnn-to-count-to-three/
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作者：[Julia Evans][a]
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选题：[lujun9972][b]
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译者：[译者ID](https://github.com/译者ID)
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校对：[校对者ID](https://github.com/校对者ID)
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本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
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[a]: https://jvns.ca/
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[b]: https://github.com/lujun9972
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[1]: https://gist.github.com/jvns/b8804fb9d0672ce147a28d22648b4bd7
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[2]: https://colab.research.google.com/gist/jvns/b8804fb9d0672ce147a28d22648b4bd7/rnn-123.ipynb
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