[Computer-go] Using 9x9 policy on 13x13 and 19x19

Imran Hendley imran.hendley at gmail.com
Thu Mar 1 07:35:46 PST 2018


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 <connerator at 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 <
> erikvanderwerf at 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 <yss at 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 at computer-go.org
> > http://computer-go.org/mailman/listinfo/computer-go
> >
>
> _______________________________________________
> Computer-go mailing list
> Computer-go at 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/20180301/b1249410/attachment.html>


More information about the Computer-go mailing list