[Computer-go] What hardware to use to train the DNN

David Fotland fotland at smart-games.com
Tue Feb 2 10:25:13 PST 2016


Detlef, Hiroshi, Hideki, and others,

I have caffelib integrated with Many Faces so I can evaluate a DNN.  Thank you very much Detlef for sample code to set up the input layer.  Building caffe on windows is painful.  If anyone else is doing it and gets stuck I might be able to help.

What hardware are you using to train networks?  I don’t have a cuda-capable GPU yet, so I'm going to buy a new box.  I'd like some advice.  Caffe is not well supported on Windows, so I plan to use a Linux box for training, but continue to use Windows for testing and development.  For competitions I could use either windows or linux.

Thanks in advance,

David

> -----Original Message-----
> From: Computer-go [mailto:computer-go-bounces at computer-go.org] On Behalf
> Of Hiroshi Yamashita
> Sent: Monday, February 01, 2016 11:26 PM
> To: computer-go at computer-go.org
> Subject: *****SPAM***** Re: [Computer-go] DCNN can solve semeai?
> 
> 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" <ds2 at physik.de>
> To: <computer-go at 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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