I’ll do training on Linux for performance, and because it is so much easier to build than on Windows. I need something I can ship to my windows customers, that is light weight enough to play well without a GPU.
All of my testing and evaluation machines and tools are on Windows, so I can’t easily measure strength and progress on linux. I’m also not eager to learn a new IDE. I like Visual Studio. David From: Computer-go [mailto:computer-go-boun...@computer-go.org] On Behalf Of Petri Pitkanen Sent: Thursday, February 04, 2016 9:12 PM To: computer-go Subject: Re: [Computer-go] What hardware to use to train the DNN Welll, David is making a product. Making a product is 'trooper' solution unless you are making very specific product to a very narrow target group, willing to pay thousands for single license Petri 2016-02-04 23:50 GMT+02:00 uurtamo . <uurt...@gmail.com>: David, You're a trooper for doing this in windows. :) The OS overhead is generally lighter if you use unix; even the most modern windows versions have a few layers of slowdown. Unix (for better or worse) will give you closer, easier access to the hardware, and closer, easier access to halting your machine if you are deep in the guts. ;) s. On Tue, Feb 2, 2016 at 10:25 AM, David Fotland <fotl...@smart-games.com> wrote: 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-boun...@computer-go.org] On Behalf > Of Hiroshi Yamashita > Sent: Monday, February 01, 2016 11:26 PM > To: computer-go@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" <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 > > _______________________________________________ > Computer-go mailing list > Computer-go@computer-go.org > http://computer-go.org/mailman/listinfo/computer-go _______________________________________________ Computer-go mailing list Computer-go@computer-go.org http://computer-go.org/mailman/listinfo/computer-go _______________________________________________ Computer-go mailing list Computer-go@computer-go.org http://computer-go.org/mailman/listinfo/computer-go
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