Hi, Winrate of your pure CNN againts pachi retsugen is:
GAMES WINRATE S.D. PAIRING 224 0.558 0.033 19-7.5-1-pachi-=10000-detlef_54 221 0.407 0.033 19-7.5-1-pachi-=20000-detlef_54 I used the https://github.com/jmoudrik/deep-go-wrap for the player. Regards, Josef On Tue, Dec 29, 2015 at 10:24 AM Detlef Schmicker <d...@physik.de> wrote: > -----BEGIN PGP SIGNED MESSAGE----- > Hash: SHA1 > > Hi, > > I am fighting with the problem most seem to have with the strong move > predictions at the moment, MCTS is not increasing the players a lot :) > > I wonder, if somebody measured the performance of the pure CNN54 > against pachi 10k (or 100k), to get a comparison with the darkforest CNN. > > It is not too much work, but you probably did it already. > > Thanks, > > Detlef > > Am 21.12.2015 um 12:42 schrieb Hiroshi Yamashita: > > Hi Detlef, > > > > Thank you for publishing your data and latest oakform code! It was > > very helpful for me. > > > > I tried your 54% data with Aya. > > > > Aya with Detlef54% vs Aya with Detlef44%, 10000 playout/move Aya > > with Detlef54%'s winrate is 0.569 (124wins / 218games). > > > > CGOS BayseElo rating Aya with Detlef44% (aya786n_Detlef_10k) 3040 > > Aya with Detlef54% (Aya786m_Det54_10k ) 3036 > > http://www.yss-aya.com/cgos/19x19/bayes.html > > > > Detlef54% is a bit stronger in selfplay, but they are similar on > > CGOS. Maybe Detlef54%'s prediction is strong, and Aya's playout > > strength is not enough. > > > > Speed for a position on GTS 450. Detlef54% 21ms Detlef44% 17ms > > > > Cumulative accuracy from 1000 pro games. > > > > move rank Aya Detlef54% Mixture 1 40.8 47.6 > > 48.0 2 53.5 62.4 62.7 3 60.2 70.7 71.0 > > 4 64.8 75.8 76.1 5 68.1 79.5 79.9 6 > > 71.0 82.3 82.6 7 73.2 84.5 84.8 8 75.2 > > 86.3 86.6 9 76.9 87.8 88.1 10 78.3 89.0 > > 89.3 11 79.6 90.2 90.6 12 80.8 91.2 > > 91.4 13 81.9 92.0 92.2 14 82.9 92.7 > > 92.9 15 83.8 93.3 93.5 16 84.6 93.9 > > 94.1 17 85.4 94.3 94.5 18 86.1 94.8 > > 95.0 19 86.8 95.2 95.4 20 87.4 95.5 > > 95.7 > > > > Mixture is pretty same as Detlef54%. I changed learning method from > > MM to LFR. Aya's own accuracy is from LFR rank, not MM gamma. So > > comparison is difficult. > > > > Cumulative accuracy Detlef44% > > http://computer-go.org/pipermail/computer-go/2015-October/008031.html > > > > Regards, Hiroshi Yamashita > > > > > > ----- Original Message ----- From: "Detlef Schmicker" > > <d...@physik.de> To: <computer-go@computer-go.org> Sent: Wednesday, > > December 09, 2015 12:13 AM Subject: [Computer-go] CNN with 54% > > prediction on KGS 6d+ data > > > > > >> -----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 > >> > >> Hi, > >> > >> as somebody ask I will offer my actual CNN for testing. > >> > >> It has 54% prediction on KGS 6d+ data (which I thought would be > >> state of the art when I started training, but it is not > >> anymore:). > >> > >> it has: 1 2 3 > >>> 4 libs playing color > >> 1 2 3 > >>> 4 libs opponent color > >> Empty points last move second last move third last move forth > >> last move > >> > >> input layers, and it is fully convolutional, so with just editing > >> the golast19.prototxt file you can use it for 13x13 as well, as I > >> did on last sunday. It was used in November tournament as well. > >> > >> You can find it http://physik.de/CNNlast.tar.gz > >> > >> > >> > >> If you try here some points I like to get discussion: > >> > >> - - it seems to me, that the playouts get much more important > >> with such a strong move prediction. Often the move prediction > >> seems better the playouts (I use 8000 at the moment against pachi > >> 32000 with about 70% winrate on 19x19, but with an extremely > >> focused progressive widening (a=400, a=20 was usual). > >> > >> - - live and death becomes worse. My interpretation is, that the > >> strong CNN does not play moves, which obviously do not help to > >> get a group life, but would help the playouts to recognize the > >> group is dead. (http://physik.de/example.sgf top black group was > >> with weaker move prediction read very dead, with good CNN it was > >> 30% alive or so :( > >> > >> > >> OK, hope you try it, as you know our engine oakfoam is open > >> source :) We just merged all the CNN stuff into the main branch! > >> https://bitbucket.org/francoisvn/oakfoam/wiki/Home > >> http://oakfoam.com > >> > >> > >> Do the very best with the CNN > >> > >> Detlef > > > > _______________________________________________ Computer-go mailing > > list Computer-go@computer-go.org > > http://computer-go.org/mailman/listinfo/computer-go > > > -----BEGIN PGP SIGNATURE----- > Version: GnuPG v2.0.22 (GNU/Linux) > > iQIcBAEBAgAGBQJWglFcAAoJEInWdHg+Znf42q4P/AnMdgqhps4RSJG3NoLiwEUq > QmT4mQd58WbuxnXRO4xiyIKGTQq13+FOpqVu7RgFPXxQaKS+8Hi1qpGVjg8aE8Zh > bnHb3D+p30hv9lCT8e4xNQ2B1JZsgOlM3MsbeFdQB+vxca3kUcnCf9oMvHo0W8TL > Tl8q7sDbI1bW0Z16lCKfDHdwyiBhDjETPP9j1wlfZgXyqD5JMCqwxcUkOrxlsh96 > ZhX5bCnbN5CAPKedTxQVz8GcPwo74TIXCb+UmzklVOBC3pGJ3WrtWmNyHiPwiJ75 > qYEzolICvW+wE+RbCfeiGaaL1CY9B5N2GKSCPQdzd0UYUwBrXsUMG3mTJ5Kwg26G > +nIg/KBnWCbgjN9WpHVkAsRewkAGezom7OSp2y1KyrIORcQc3FW8LLWxhzXjBNuj > 3VFx9iT6zSiO+5kjUINdejVh4cT19Oao+ZVWZuPyBf9y/dcUn01NE2tCr+xIcqFq > 7p+R0y9VA15f/KDufgJHUeeaPCdox6YU4VlxlbQoKdQt/X6iQftxPEDcBe39kxRy > R7SGJ6sMYxJBbsnNFfb547jBpeJRunHaX2dswjZtKleEUSTGXKgs77/ju3kbgC8n > WZuvvs6QcPqPsAyFFBsYbpOelP2NT7jpMX7IdkiGLb5wpblUhtnkV+nTy2ccTG1e > veWukuo97oFFhUBSHQtV > =+mE4 > -----END PGP SIGNATURE----- > _______________________________________________ > Computer-go mailing list > Computer-go@computer-go.org > http://computer-go.org/mailman/listinfo/computer-go
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