From a6afd8247bd9b56c26b9838a2626b3bc101ba9ea Mon Sep 17 00:00:00 2001
From: DarkSun <lujun9972@gmail.com>
Date: Sun, 22 Nov 2020 05:02:49 +0800
Subject: [PATCH] =?UTF-8?q?=E9=80=89=E9=A2=98[tech]:=2020201120=20Day=2010?=
 =?UTF-8?q?:=20Training=20an=20RNN=20to=20count=20to=20three?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

sources/tech/20201120 Day 10- Training an RNN to count to three.md
---
 ...y 10- Training an RNN to count to three.md | 56 +++++++++++++++++++
 1 file changed, 56 insertions(+)
 create mode 100644 sources/tech/20201120 Day 10- Training an RNN to count to three.md

diff --git a/sources/tech/20201120 Day 10- Training an RNN to count to three.md b/sources/tech/20201120 Day 10- Training an RNN to count to three.md
new file mode 100644
index 0000000000..a8352615fb
--- /dev/null
+++ b/sources/tech/20201120 Day 10- Training an RNN to count to three.md	
@@ -0,0 +1,56 @@
+[#]: collector: (lujun9972)
+[#]: translator: ( )
+[#]: reviewer: ( )
+[#]: publisher: ( )
+[#]: url: ( )
+[#]: subject: (Day 10: Training an RNN to count to three)
+[#]: via: (https://jvns.ca/blog/2020/11/20/day-10--training-an-rnn-to-count-to-three/)
+[#]: author: (Julia Evans https://jvns.ca/)
+
+Day 10: Training an RNN to count to three
+======
+
+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
+
+```
+0 1 2 0 1 2 0 1 2 0 1 2
+```
+
+and slightly more complicated sequences like
+
+```
+0 1 2 1 0 1 2 1 0 1 2 1 0 1 2 1 0
+```
+
+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).
+
+### the jupyter notebook
+
+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.
+
+  * Here it is as a [github gist][1]
+  * and [here it is on Colab][2] if you want to run it yourself
+
+
+
+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.
+
+### simplifying is nice
+
+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.
+
+--------------------------------------------------------------------------------
+
+via: https://jvns.ca/blog/2020/11/20/day-10--training-an-rnn-to-count-to-three/
+
+作者：[Julia Evans][a]
+选题：[lujun9972][b]
+译者：[译者ID](https://github.com/译者ID)
+校对：[校对者ID](https://github.com/校对者ID)
+
+本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
+
+[a]: https://jvns.ca/
+[b]: https://github.com/lujun9972
+[1]: https://gist.github.com/jvns/b8804fb9d0672ce147a28d22648b4bd7
+[2]: https://colab.research.google.com/gist/jvns/b8804fb9d0672ce147a28d22648b4bd7/rnn-123.ipynb