diff options
Diffstat (limited to 'include/cnn1986.c')
| -rw-r--r-- | include/cnn1986.c | 71 |
1 files changed, 40 insertions, 31 deletions
diff --git a/include/cnn1986.c b/include/cnn1986.c index 9191ec8..b5bedfa 100644 --- a/include/cnn1986.c +++ b/include/cnn1986.c @@ -22,6 +22,7 @@ // 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]; @@ -29,29 +30,37 @@ static float dense2[DENSE2_NUM]; #define RELU(x) ((x) = ((x)<0)?0:(x)) float cnn1986_evaluate_black_win(void) { /* --------------- * - * Generate input * + * 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]; - float cur_board[board_size*board_size][KERN_CHAN]; - for (int y = 0; y < board_size; y++) { - for (int x = 0; x < board_size; x++) { - const int loc = x+y*board_size; - for (int c = 0; c < KERN_CHAN; c++) { // heh, c++ + if (count > 0) { float lookup = 0; - if (COUNT_AT(loc)>c) { - if (c==0) { - if (STONE_AT(loc) == STONE_STANDING) { - lookup = (colours[loc] & 1) ? +0.25 : -0.25; - } else if (STONE_AT(loc) == STONE_CAPSTONE) { - lookup = (colours[loc] & 1) ? +1.00 : -1.00; - } else { - lookup = (colours[loc] & 1) ? +0.50 : -0.50; - } - } else { - lookup = (colours[loc] & (1<<c)) ? +0.50 : -0.50; - } + 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; } - cur_board[loc][c] = lookup; } } } @@ -59,18 +68,18 @@ float cnn1986_evaluate_black_win(void) { * Convolution layer * * ------------------ */ // for each kernel - for (uint8_t kern = 0; kern < KERN_NUM; kern++) { + for (unsigned int kern = 0; kern < KERN_NUM; kern++) { // the stride is 1, march across the board - for (uint8_t bx = 0; bx < KERN_OSIZE; bx++) { - for (uint8_t by = 0; by < KERN_OSIZE; by++) { + 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 (uint8_t ky = 0; ky < KERN_SIZE; ky++) { - for (uint8_t kx = 0; kx < KERN_SIZE; kx++) { - for (uint8_t c = 0; c < KERN_CHAN; c++) { + 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 uint8_t loc = kx+bx+(ky+by)*board_size; + 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]; } @@ -86,9 +95,9 @@ float cnn1986_evaluate_black_win(void) { /* ------------------ * * First dense layer * * ------------------ */ - for (uint8_t d1 = 0; d1 < DENSE1_NUM; d1++) { + for (unsigned int d1 = 0; d1 < DENSE1_NUM; d1++) { dense1[d1] = dense1_biases[d1]; - for (uint8_t fl = 0; fl < CONV_NUM+2; fl++) { + for (unsigned int fl = 0; fl < CONV_NUM+2; fl++) { dense1[d1] += flattened[fl]*dense1_weights[d1][fl]; } RELU(dense1[d1]); @@ -96,9 +105,9 @@ float cnn1986_evaluate_black_win(void) { /* ------------------- * * Second dense layer * * ------------------- */ - for (uint8_t d2 = 0; d2 < DENSE2_NUM; d2++) { + for (unsigned int d2 = 0; d2 < DENSE2_NUM; d2++) { dense2[d2] = dense2_biases[d2]; - for (uint8_t d1 = 0; d1 < DENSE1_NUM; d1++) { + for (unsigned int d1 = 0; d1 < DENSE1_NUM; d1++) { dense2[d2] += dense1[d1]*dense2_weights[d2][d1]; } RELU(dense2[d2]); @@ -107,7 +116,7 @@ float cnn1986_evaluate_black_win(void) { * Output layer * * ------------- */ float output = output_bias; - for (uint8_t d2 = 0; d2 < DENSE2_NUM; d2++) { + for (unsigned int d2 = 0; d2 < DENSE2_NUM; d2++) { output += dense2[d2]*output_weights[d2]; } // 2*(clamped Pade approximant of logistic function) - 1 |