blob: a837a59c98f9517e5a18138dd2d6865cc70605c1 (
plain)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
|
/*
This file is part of ct.
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
General Public License for more details.
You should have received a copy of the GNU General Public License
along with ct. If not, see <https://www.gnu.org/licenses/>.
*/
#include "nn.h"
#include "tak.h"
#include "weights_5.h"
#include "weights_6.h"
// ===================================================================
// Helpers
// ===================================================================
#define RELU(x) ((x) = ((x) < 0) ? 0 : (x))
static inline void prepare_input(float *cur_board, tak_state_p stat);
// ===================================================================
// Implementation of small neural networks
// ===================================================================
float nn1986_evaluate_black_win(tak_state_p state) {
static float cur_board[FIVE_INP_NUM];
static float dense1[FIVE_DENSE_NUM];
static float output[2];
prepare_input(cur_board, state);
/* ------------------ *
* First dense layer *
* ------------------ */
for (unsigned int d1 = 0; d1 < FIVE_DENSE_NUM; d1++) {
dense1[d1] = five_dense_biases[d1];
for (unsigned int fl = 0; fl < FIVE_INP_NUM; fl++) {
dense1[d1] += cur_board[fl] * five_dense_weights[d1][fl];
}
RELU(dense1[d1]);
}
/* ------------- *
* Output layer *
* ------------- */
for (uint8_t k = 0; k < 2; k++) {
output[k] = five_output_bias[k];
for (unsigned int d2 = 0; d2 < FIVE_DENSE_NUM; d2++) {
output[k] += dense1[d2] * five_output_weights[k][d2];
}
RELU(output[k]);
}
const float norm = output[0] + output[1];
return (2 * output[0] / norm) - 1;
}
float nn2690_evaluate_black_win(tak_state_p state) {
static float cur_board[SIX_INP_NUM];
static float dense1[SIX_DENSE_NUM];
static float output[2];
prepare_input(cur_board, state);
/* ------------------ *
* First dense layer *
* ------------------ */
for (unsigned int d1 = 0; d1 < SIX_DENSE_NUM; d1++) {
dense1[d1] = six_dense_biases[d1];
for (unsigned int fl = 0; fl < SIX_INP_NUM; fl++) {
dense1[d1] += cur_board[fl] * six_dense_weights[d1][fl];
}
RELU(dense1[d1]);
}
/* ------------- *
* Output layer *
* ------------- */
for (uint8_t k = 0; k < 2; k++) {
output[k] = six_output_bias[k];
for (unsigned int d2 = 0; d2 < SIX_DENSE_NUM; d2++) {
output[k] += dense1[d2] * six_output_weights[k][d2];
}
RELU(output[k]);
}
const float norm = output[0] + output[1];
return (2 * output[0] / norm) - 1;
}
// ===================================================================
// Helper implementation
// ===================================================================
static inline void prepare_input(float *cur_board, tak_state_p state) {
/* --------------- *
* Populate input *
* --------------- */
for (unsigned int y = 0; y < state->board_size; y++) {
for (unsigned int x = 0; x < state->board_size; x++) {
const unsigned int loc = x + y * state->board_size;
const unsigned int count = COUNT_AT(state, loc);
float lookup = 0;
if (count > 0) {
if (STONE_AT(state, loc) == STONE_STANDING) {
lookup = (state->colours[loc] & 1) ? +0.25 : -0.25;
} else if (STONE_AT(state, loc) == STONE_CAPSTONE) {
lookup = (state->colours[loc] & 1) ? +1.00 : -1.00;
} else {
lookup = (state->colours[loc] & 1) ? +0.50 : -0.50;
}
}
cur_board[3 + loc] = lookup;
}
}
// Add input of flat counts and ply parity
cur_board[0] = (state->ply & 1) ? 1 : -1;
cur_board[1] = (float)(state->white_count & 127) / 21.0;
cur_board[2] = (float)(state->black_count & 127) / 21.0;
}
|
