Hi Detlef,
My study heavily depends on your information. Especially Oakfoam code,
lenet.prototxt and generate_sample_data_leveldb.py was helpful. Thanks!
Quite interesting that you do not reach the prediction rate 57% from
the facebook paper by far too! I have the same experience with the
I'm trying 12 layers 256 filters, but it is around 49.8%.
I think 57% is maybe from KGS games.
Did you strip the games before 1800AD, as mentioned in the FB paper? I
did not do it and was thinking my training is not ok, but as you have
the same result probably this is the only difference?!
I also did not use before 1800AD. And don't use hadicap games.
Training positions are 15693570 from 76000 games.
Test positions are 445693 from 2156 games.
All games are shuffled in advance. Each position is randomly rotated.
And memorizing 24000 positions, then shuffle and store to LebelDB.
At first I did not shuffle games. Then accuracy is down each 61000
iteration (one epoch, 256 mini-batch).
http://www.yss-aya.com/20160108.png
It means DCNN understands easily the difference 1800AD games and
2015AD games. I was surprised DCNN's ability. And maybe 1800AD games
are also not good for training?
Regards,
Hiroshi Yamashita
----- Original Message -----
From: "Detlef Schmicker" <d...@physik.de>
To: <computer-go@computer-go.org>
Sent: Tuesday, February 02, 2016 3:15 PM
Subject: Re: [Computer-go] DCNN can solve semeai?
Thanks a lot for sharing this.
Quite interesting that you do not reach the prediction rate 57% from
the facebook paper by far too! I have the same experience with the
GoGoD database. My numbers are nearly the same as yours 49% :) my net
is quite simelar, but I use 7,5,5,3,3,.... with 12 layers in total.
Did you strip the games before 1800AD, as mentioned in the FB paper? I
did not do it and was thinking my training is not ok, but as you have
the same result probably this is the only difference?!
Best regards,
Detlef
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