Hi Gian-Carlo Pascutto, My mistake. The model file didn't get pushed. It is now in train/rl_framework/examples/go/models/
Best, Yuandong On Wed, Oct 5, 2016 at 5:00 AM, <computer-go-requ...@computer-go.org> wrote: > Send Computer-go mailing list submissions to > computer-go@computer-go.org > > To subscribe or unsubscribe via the World Wide Web, visit > http://computer-go.org/mailman/listinfo/computer-go > or, via email, send a message with subject or body 'help' to > computer-go-requ...@computer-go.org > > You can reach the person managing the list at > computer-go-ow...@computer-go.org > > When replying, please edit your Subject line so it is more specific > than "Re: Contents of Computer-go digest..." > > > Today's Topics: > > 1. DarkForest policy network training code is open-source now. > (Yuandong Tian) > 2. Re: DarkForest policy network training code is open-source > now. (Thomas Rohde) > 3. Zen19K2 is strongest player on KGS (Hiroshi Yamashita) > 4. Re: Zen19K2 is strongest player on KGS (uurtamo .) > 5. Re: DarkForest policy network training code is open-source > now. (Gian-Carlo Pascutto) > > > ---------------------------------------------------------------------- > > Message: 1 > Date: Tue, 4 Oct 2016 14:47:30 -0700 > From: Yuandong Tian <yuandong.t...@gmail.com> > To: computer-go@computer-go.org > Subject: [Computer-go] DarkForest policy network training code is > open-source now. > Message-ID: > <CAA9V9Gxv98nDxTLk3FsK0aV+jy1JDJYg-YR47nUtxvuJd5MKUQ@ > mail.gmail.com> > Content-Type: text/plain; charset="utf-8" > > Hi all, > > DarkForest training code is open source now. Hopefully it will help the > community. > > https://github.com/facebookresearch/darkforestGo > > With 4 GPUs, the training procedure gives 56.1% top-1 accuracy in KGS > dataset in 3.5 days, and 57.1% top-1 in 6.5 days (see the simple log > below). The parameters used are the following: --epoch_size 256000 --GPU 4 > --data_augmentation --alpha 0.1 --nthread 4 > > | Sun Aug 21 21:54:15 2016 | epoch 0001 | ms/batch 721 | train > [1pi@1]: 11.230860 [1pi@5]: 30.617970 [3pi@1]: 3.099219 [3pi@5]: > 14.042188 [2pi@5]: 18.935938 [2pi@1]: 4.482813 [policy]: 8.361849 > | test [1pi@1]: 27.767189 [1pi@5]: 59.403130 [3pi@1]: 5.380469 > [3pi@5]: 24.729689 [2pi@5]: 34.030472 [2pi@1]: 8.382812 [policy]: > 6.558414 | saved * > > | Thu Aug 25 10:35:11 2016 | epoch 0381 | ms/batch 719 | train > [1pi@1]: 56.226566 [1pi@5]: 87.523834 [3pi@1]: 21.542580 [3pi@5]: > 51.992970 [2pi@5]: 68.728127 [2pi@1]: 34.199612 [policy]: 3.736506 > | test [1pi@1]: 56.124222 [1pi@5]: 87.432816 [3pi@1]: 21.600000 > [3pi@5]: 52.107815 [2pi@5]: 68.922661 [2pi@1]: 34.421875 [policy]: > 3.737540 | saved * > > | Sun Aug 28 00:49:32 2016 | epoch 0661 | ms/batch 721 | train > [1pi@1]: 57.075783 [1pi@5]: 88.215240 [3pi@1]: 22.512892 [3pi@5]: > 53.472267 [2pi@5]: 70.093361 [2pi@1]: 35.576565 [policy]: 3.638625 > | test [1pi@1]: 57.101566 [1pi@5]: 88.271095 [3pi@1]: 22.295313 > [3pi@5]: 53.226566 [2pi@5]: 70.085938 [2pi@1]: 35.185940 [policy]: > 3.646803 | saved > > Thanks! > > Best, > Yuandong > > ---------------------------- > Yuandong Tian > Research Scientist, > Facebook Artificial Intelligence Research (FAIR) > Website: > https://research.facebook.com/researchers/1517678171821436/yuandong-tian/ > -------------- next part -------------- > An HTML attachment was scrubbed... > URL: <http://computer-go.org/pipermail/computer-go/ > attachments/20161004/e96304c7/attachment-0001.html> > > ------------------------------ > > Message: 2 > Date: Wed, 5 Oct 2016 01:56:18 +0200 > From: Thomas Rohde <t...@bonobo.com> > To: computer-go@computer-go.org > Subject: Re: [Computer-go] DarkForest policy network training code is > open-source now. > Message-ID: <461b50e8-bb6b-46bb-b481-074773308...@bonobo.com> > Content-Type: text/plain; charset=utf-8 > > Thank you, Yuangdong Tian, > > > On 2016-10-04 at 23:47, Yuandong Tian <yuandong.t...@gmail.com> wrote: > > > DarkForest training code is open source now. Hopefully it will help the > community. > > > > [..] > > spreading this wide and far, with high hopes that we’ll soon see many more > strong Go apps! > > > Greetings, Tom > -- > Thomas Rohde > Wiesenkamp 12, 29646 Bispingen > ---------------- > t...@bonobo.com > > ------------------------------ > > Message: 3 > Date: Wed, 5 Oct 2016 12:02:15 +0900 > From: "Hiroshi Yamashita" <y...@bd.mbn.or.jp> > To: <computer-go@computer-go.org> > Subject: [Computer-go] Zen19K2 is strongest player on KGS > Message-ID: <A78021954DF948F78FE96480DA1A7BE9@i3540> > Content-Type: text/plain; format=flowed; charset="iso-2022-jp"; > reply-type=original > > Hi, > > Zen19K2 is strongest player on KGS. > http://www.gokgs.com/top100.jsp > Oops, another player is top now. But anyway nearly top. > > Zen19K2 is maybe 10.3d from graph. > http://www.gokgs.com/graphPage.jsp?user=Zen19K2 > > Zen19K2's information is > ------------------------------------- > Computer program Zen running on KURISU server provided by DWANGO. > > CPU: Xeon E5-2623 v3 x2 > GPU: GeForce GTX TITAN X x4 > > The number of handicap stones is limited to 3 or less. > ------------------------------------- > > Congratulations for graduation from KGS, Zen! > I think Zen19K2 strength is similar to 2015/10 AlphaGo that beated Fan Hui > 2p, 5-0. > > Thanks, > Hiroshi Yamashita > > > > ------------------------------ > > Message: 4 > Date: Tue, 4 Oct 2016 20:15:51 -0700 > From: "uurtamo ." <uurt...@gmail.com> > To: computer-go <computer-go@computer-go.org> > Subject: Re: [Computer-go] Zen19K2 is strongest player on KGS > Message-ID: > <CADg0iNAW0fKWaiEP48DW8-DbG9hOEKmrEHW4rgFLr1_ztWOS2g@ > mail.gmail.com> > Content-Type: text/plain; charset="utf-8" > > This is really good to hear. > > 3 stones is totally reasonable. > > s. > > On Oct 4, 2016 8:02 PM, "Hiroshi Yamashita" <y...@bd.mbn.or.jp> wrote: > > > Hi, > > > > Zen19K2 is strongest player on KGS. > > http://www.gokgs.com/top100.jsp > > Oops, another player is top now. But anyway nearly top. > > > > Zen19K2 is maybe 10.3d from graph. > > http://www.gokgs.com/graphPage.jsp?user=Zen19K2 > > > > Zen19K2's information is ------------------------------------- > > Computer program Zen running on KURISU server provided by DWANGO. > > > > CPU: Xeon E5-2623 v3 x2 > > GPU: GeForce GTX TITAN X x4 > > > > The number of handicap stones is limited to 3 or less. > > ------------------------------------- > > > > Congratulations for graduation from KGS, Zen! > > I think Zen19K2 strength is similar to 2015/10 AlphaGo that beated Fan > Hui > > 2p, 5-0. > > > > Thanks, > > Hiroshi Yamashita > > > > _______________________________________________ > > Computer-go mailing list > > Computer-go@computer-go.org > > http://computer-go.org/mailman/listinfo/computer-go > -------------- next part -------------- > An HTML attachment was scrubbed... > URL: <http://computer-go.org/pipermail/computer-go/ > attachments/20161004/6cf69c3c/attachment-0001.html> > > ------------------------------ > > Message: 5 > Date: Wed, 5 Oct 2016 11:29:44 +0200 > From: Gian-Carlo Pascutto <g...@sjeng.org> > To: computer-go@computer-go.org > Subject: Re: [Computer-go] DarkForest policy network training code is > open-source now. > Message-ID: <5b82f293-9d44-8623-83ee-302799fc0...@sjeng.org> > Content-Type: text/plain; charset=utf-8 > > On 04-10-16 23:47, Yuandong Tian wrote: > > Hi all, > > > > DarkForest training code is open source now. Hopefully it will help the > > community. > > > > https://github.com/facebookresearch/darkforestGo > > <https://github.com/facebookresearch/darkforestGo> > > > > With 4 GPUs, the training procedure gives 56.1% top-1 accuracy in KGS > > dataset in 3.5 days, and 57.1% top-1 in 6.5 days (see the simple log > > below). The parameters used are the following: --epoch_size 256000 --GPU > > 4 --data_augmentation --alpha 0.1 --nthread 4 > > It's probably due to my unfamiliarity with Torch but I couldn't find > where the actual network structure is defined. > > I think the script runs with alpha=0.05, not alpha=0.1. > > I understood from previous comments you didn't find momentum to be > beneficial. This highly surprises me. Is that still the case? > > -- > GCP > > > ------------------------------ > > Subject: Digest Footer > > _______________________________________________ > Computer-go mailing list > Computer-go@computer-go.org > http://computer-go.org/mailman/listinfo/computer-go > > ------------------------------ > > End of Computer-go Digest, Vol 81, Issue 4 > ****************************************** > -- ---------------------------- Yuandong Tian Research Scientist, Facebook Artificial Intelligence Research (FAIR) Website: https://research.facebook.com/researchers/1517678171821436/yuandong-tian/
_______________________________________________ Computer-go mailing list Computer-go@computer-go.org http://computer-go.org/mailman/listinfo/computer-go
