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diff --git a/include/nn.c b/include/nn.c
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-/*
- 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;
-}