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Diffstat (limited to 'include/cnn1986.c')
| -rw-r--r-- | include/cnn1986.c | 131 |
1 files changed, 0 insertions, 131 deletions
diff --git a/include/cnn1986.c b/include/cnn1986.c deleted file mode 100644 index 7c7dbcb..0000000 --- a/include/cnn1986.c +++ /dev/null @@ -1,131 +0,0 @@ -/* - 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 "cnn1986.h" -#include "weights.h" - -// =================================================================== -// Implementation of a small convolutional neural network -// =================================================================== - -static float cur_board[5*5][KERN_CHAN]; -static float flattened[CONV_NUM+2]; -static float dense1[DENSE1_NUM]; -static float dense2[DENSE2_NUM]; - -#define RELU(x) ((x) = ((x)<0)?0:(x)) -float cnn1986_evaluate_black_win(void) { - /* --------------- * - * Populate input * - * --------------- */ - for (unsigned int y = 0; y < board_size; y++) { - for (unsigned int x = 0; x < board_size; x++) { - const unsigned int loc = x+y*board_size; - const unsigned int count = COUNT_AT(loc); - colour_stack_t colour = colours[loc]; - - if (count > 0) { - float lookup = 0; - if (STONE_AT(loc) == STONE_STANDING) { - lookup = (colour & 1) ? +0.25 : -0.25; - } else if (STONE_AT(loc) == STONE_CAPSTONE) { - lookup = (colour & 1) ? +1.00 : -1.00; - } else { - lookup = (colour & 1) ? +0.50 : -0.50; - } - cur_board[loc][0] = lookup; - - colour>>=1; - for (unsigned int c = 1; c < count && c < KERN_CHAN; c++, colour>>=1) { - cur_board[loc][c] = (colour & 1) ? +0.50 : -0.50; - } - - for (unsigned int c = count; c < KERN_CHAN; c++) { - cur_board[loc][c] = 0; - } - } else { - for (unsigned int c = 0; c < KERN_CHAN; c++) { - cur_board[loc][c] = 0; - } - } - } - } - /* ------------------ * - * Convolution layer * - * ------------------ */ - // for each kernel - for (unsigned int kern = 0; kern < KERN_NUM; kern++) { - // the stride is 1, march across the board - for (unsigned int bx = 0; bx < KERN_OSIZE; bx++) { - for (unsigned int by = 0; by < KERN_OSIZE; by++) { - flattened[kern+KERN_NUM*(bx+by*KERN_OSIZE)] = - conv2d_biases[kern]; - // Compute the convolution for this position - for (unsigned int ky = 0; ky < KERN_SIZE; ky++) { - for (unsigned int kx = 0; kx < KERN_SIZE; kx++) { - for (unsigned int c = 0; c < KERN_CHAN; c++) { - // Where we are on the board - const unsigned int loc = kx+bx+(ky+by)*board_size; - flattened[kern+KERN_NUM*(bx+by*KERN_OSIZE)] - += cur_board[loc][c]*conv2d_weights[kern][ky][kx][c]; - } - } - } - RELU(flattened[kern+KERN_NUM*(bx+by*KERN_OSIZE)]); - } - } - } - // Add input of flat counts - flattened[CONV_NUM] = (float)(white_count & 127)/21.0; - flattened[CONV_NUM+1] = (float)(black_count & 127)/21.0; - /* ------------------ * - * First dense layer * - * ------------------ */ - for (unsigned int d1 = 0; d1 < DENSE1_NUM; d1++) { - dense1[d1] = dense1_biases[d1]; - for (unsigned int fl = 0; fl < CONV_NUM+2; fl++) { - dense1[d1] += flattened[fl]*dense1_weights[d1][fl]; - } - RELU(dense1[d1]); - } - /* ------------------- * - * Second dense layer * - * ------------------- */ - for (unsigned int d2 = 0; d2 < DENSE2_NUM; d2++) { - dense2[d2] = dense2_biases[d2]; - for (unsigned int d1 = 0; d1 < DENSE1_NUM; d1++) { - dense2[d2] += dense1[d1]*dense2_weights[d2][d1]; - } - RELU(dense2[d2]); - } - /* ------------- * - * Output layer * - * ------------- */ - float output = output_bias; - for (unsigned int d2 = 0; d2 < DENSE2_NUM; d2++) { - output += dense2[d2]*output_weights[d2]; - } - // 2*(clamped Pade approximant of logistic function) - 1 - output = (12.0+output+50.0*output/(output*output+10.0))/12.0 - 1.0; - if (output > 1.0) { - return 1.0; - } - else if (output < -1.0) { - return -1.0; - } - return output; -} |