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
