Hi Hiroshi, Are you using zero-padding to allow input shapes to match for all board sizes?
Thanks, Imran On Thu, Mar 1, 2018 at 12:06 AM, Cornelius <connera...@gmx.de> wrote: > Hi Sighris, > > i have always thought that creating algorithms for arbitrary large go > boards should enlighten us in regards to playing on smaller go boards. > > A humans performance doesn't differ that much on differently sized large > go boards and it scales pretty well. For example one would find it > rather easy to evaluate large dragons or ladders. The currently used > neural algorithms (NNs) do not perform well in this regard. > > Maybe some form of RNN could be integrated into the evaluation. > > > BR, > Cornelius > > > Am 24.02.2018 um 08:23 schrieb Sighris: > > I'm curious, does anybody have any interest in programs for 23x23 (or > > larger) Go boards? > > > > BR, > > Sighris > > > > > > On Fri, Feb 23, 2018 at 8:58 AM, Erik van der Werf < > erikvanderw...@gmail.com > >> wrote: > > > >> In the old days I trained separate move predictors on 9x9 games and on > >> 19x19 games. In my case, the ones trained on 19x19 games beat the ones > >> trained on 9x9 games also on the 9x9 board. Perhaps it was just because > of > >> was having better data from 19x19, but I thought it was interesting to > see > >> that the 19x19 predictor generalized well to smaller boards. > >> > >> I suppose the result you see can easily be explained; the big board > policy > >> learns about large scale and small scale fights, while the small board > >> policy doesn't know anything about large scale fights. > >> > >> BR, > >> Erik > >> > >> > >> On Fri, Feb 23, 2018 at 5:11 PM, Hiroshi Yamashita <y...@bd.mbn.or.jp> > >> wrote: > >> > >>> Hi, > >>> > >>> Using 19x19 policy on 9x9 and 13x13 is effective. > >>> But opposite is? > >>> I made 9x9 policy from Aya's 10k playout/move selfplay. > >>> > >>> Using 9x9 policy on 13x13 and 19x19 > >>> 19x19 DCNNAyaF128from9x9 1799 > >>> 13x13 DCNNAyaF128from9x9 1900 > >>> 9x9 DCNN_AyaF128a558x1 2290 > >>> > >>> Using 19x19 policy on 9x9 and 13x13 > >>> 19x19 DCNN_AyaF128a523x1 2345 > >>> 13x13 DCNNAya795F128a523 2354 > >>> 9x9 DCNN_AyaF128a523x1 2179 > >>> > >>> 19x19 policy is similar strength on 13x13 and 166 Elo weaker on 9x9. > >>> 9x9 policy is 390 Elo weaker on 13x13, and 491 Elo weaker on 19x19. > >>> It seems smaller board is more useless than bigger board... > >>> > >>> Note: > >>> All programs select maximum policy without search. > >>> All programs use opening book. > >>> 19x19 policy is Filter128, Layer 12, without Batch Normalization. > >>> 9x9 policy is Filter128, Layer 11, without Batch Normalization. > >>> 19x19 policy is made from pro 78000 games, GoGoD. > >>> 9x9 policy is made from 10k/move. It is CGOS 2892(Aya797c_p1v1_10k). > >>> Ratings are BayesElo. > >>> > >>> Thanks, > >>> Hiroshi Yamashita > >>> > >>> _______________________________________________ > >>> Computer-go mailing list > >> > >> > >> _______________________________________________ > >> Computer-go mailing list > > > > > > > > _______________________________________________ > > 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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