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| author | tslil <tslil@posteo.de> | 2026-09-10 14:12:41 +0100 |
|---|---|---|
| committer | tslil <tslil@posteo.de> | 2026-09-10 21:04:40 +0100 |
| commit | 220a4b79e4b6d742a9ca7c278a1962b870500fbc (patch) | |
| tree | ba527c7ec1feac91c5d59772c05fe9d32af6bdb8 /include/weights_6.h | |
| parent | 710d5a5225edb44d171ca9ac6345e9a61aa4f125 (diff) | |
credit Mitchell Riley: we can drop 65 parameters for about the same strength; also train for longer
The idea is that the current model is M_theta(board, ply) = "score for player 0", but we can instead create a different model M'_theta(board) = "score for player 0 assuming that it's player 0's move" and for a certain pair of functions sigma, normalise, recover M_theta(board, ply) = sigma(ply) M'_theta(normalise(board, ply)).
This pair of functions is sigma(ply) = +/- 1 (parity), and normalise being a conditional interchange of pieces on the board depending on the parity of the ply. The only things stopping this from being completely straightforward is that nn_train.py evaluates M'_theta itself so we need to pass sigma(ply) to the training loop.
6x6 is still untrained.
Diffstat (limited to 'include/weights_6.h')
| -rw-r--r-- | include/weights_6.h | 2 |
1 files changed, 1 insertions, 1 deletions
diff --git a/include/weights_6.h b/include/weights_6.h index d1a7c99..d927748 100644 --- a/include/weights_6.h +++ b/include/weights_6.h @@ -18,7 +18,7 @@ #ifndef WEIGHTS_6_H #define WEIGHTS_6_H -#define SIX_INP_NUM (3 + 6 * 6) +#define SIX_INP_NUM (2 + 6 * 6) #define SIX_DENSE_NUM 64 #define NN_ALIGNED __attribute__((aligned(16))) |