Points at the center of the board indeed depends on the full board, but points near the edge does not.
On Fri, Mar 11, 2016 at 3:03 PM Vincent Zhuang <[email protected]> wrote: > A stack of 11 3x3 convolutional layers and a single 5x5 layer with no > pooling actually corresponds to effectively a 27x27 kernel, which is > obviously large enough to cover the entire board. (Your value of 13 is only > the distance from the center of the filter to the edge). > > > On Thu, Mar 10, 2016 at 10:48 PM, Huazuo Gao <[email protected]> wrote: > >> According to the paper *Mastering the Game of Go with Deep Neural >> Networks and **Tree Search*, the main part of both the policy and value >> network is a 5*5 conv layer followed by eleven 3*3 conv layer. Therefore, >> after the last conv layer, the maximum "information propagation length" is >> (5-1)/2 + 11*(3-1)/2 = 13, which is insufficient for covering the full >> board. >> >> It might not have been a big problem though, as tree search and MC >> rollouts should mitigate most deficiencies to a large extent. However, >> during the opening, realising the correlation between distant stones would >> be quite important, provided that tree search would not help much while MC >> rollouts might not provide a unbiased view. >> >> It seems to me that DCNN are not perfect for Go. Anyway, apparently >> that's enough for beating top human level. >> >> _______________________________________________ >> Computer-go mailing list >> [email protected] >> http://computer-go.org/mailman/listinfo/computer-go >> > > _______________________________________________ > Computer-go mailing list > [email protected] > http://computer-go.org/mailman/listinfo/computer-go
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