void predict_move(network net, float *board, float *move, int multi)
{
float *output = network_predict(net, board);
copy_cpu(19*19, output, 1, move, 1);
int i;
if(multi){
image bim = float_to_image(19, 19, 1, board);
for(i = 1; i < 8; ++i){
rotate_image_cw(bim, i);
if(i >= 4) flip_image(bim);
float *output = network_predict(net, board);
image oim = float_to_image(19, 19, 1, output);
if(i >= 4) flip_image(oim);
rotate_image_cw(oim, -i);
axpy_cpu(19*19, 1, output, 1, move, 1);
if(i >= 4) flip_image(bim);
rotate_image_cw(bim, -i);
}
scal_cpu(19*19, 1./8., move, 1);
}
for(i = 0; i < 19*19; ++i){
if(board[i]) move[i] = 0;
}
}
void mkimg(char *cfgfile, char *weightfile, int h, int w, int num, char *prefix)
{
network *net = load_network(cfgfile, weightfile, 0);
image *ims = get_weights(net->layers[0]);
int n = net->layers[0].n;
int z;
for(z = 0; z < num; ++z){
image im = make_image(h, w, 3);
fill_image(im, .5);
int i;
for(i = 0; i < 100; ++i){
image r = copy_image(ims[rand()%n]);
rotate_image_cw(r, rand()%4);
random_distort_image(r, 1, 1.5, 1.5);
int dx = rand()%(w-r.w);
int dy = rand()%(h-r.h);
ghost_image(r, im, dx, dy);
free_image(r);
}
char buff[256];
sprintf(buff, "%s/gen_%d", prefix, z);
save_image(im, buff);
free_image(im);
}
free_network(net);
}
void random_go_moves(moves m, float *boards, float *labels, int n)
{
int i;
memset(labels, 0, 19*19*n*sizeof(float));
for(i = 0; i < n; ++i){
char *b = m.data[rand()%m.n];
int row = b[0];
int col = b[1];
labels[col + 19*(row + i*19)] = 1;
string_to_board(b+2, boards+i*19*19);
boards[col + 19*(row + i*19)] = 0;
int flip = rand()%2;
int rotate = rand()%4;
image in = float_to_image(19, 19, 1, boards+i*19*19);
image out = float_to_image(19, 19, 1, labels+i*19*19);
if(flip){
flip_image(in);
flip_image(out);
}
rotate_image_cw(in, rotate);
rotate_image_cw(out, rotate);
}
}
void test_go(char *filename, char *weightfile, int multi)
{
network net = parse_network_cfg(filename);
if(weightfile){
load_weights(&net, weightfile);
}
srand(time(0));
set_batch_network(&net, 1);
float *board = calloc(19*19, sizeof(float));
float *move = calloc(19*19, sizeof(float));
int color = 1;
while(1){
float *output = network_predict(net, board);
fltcpy(move, output, 19 * 19);
int i;
if(multi){
image bim = float_to_image(19, 19, 1, board);
for(i = 1; i < 8; ++i){
rotate_image_cw(bim, i);
if(i >= 4) flip_image(bim);
float *output = network_predict(net, board);
image oim = float_to_image(19, 19, 1, output);
if(i >= 4) flip_image(oim);
rotate_image_cw(oim, -i);
fltadd(move, output, 19 * 19);
if(i >= 4) flip_image(bim);
rotate_image_cw(bim, -i);
}
scal_cpu(19*19, 1./8., move, 1);
}
for(i = 0; i < 19*19; ++i){
if(board[i]) move[i] = 0;
}
int indexes[nind];
int row, col;
top_k(move, 19*19, nind, indexes);
print_board(board, color, indexes);
for(i = 0; i < nind; ++i){
int index = indexes[i];
row = index / 19;
col = index % 19;
printf("%d: %c %d, %.2f%%\n", i+1, col + 'A' + 1*(col > 7 && noi), (inverted)?19 - row : row+1, move[index]*100);
}
if(color == 1) printf("\u25EF Enter move: ");
else printf("\u25C9 Enter move: ");
char c;
char *line = fgetl(stdin);
int picked = 1;
int dnum = sscanf(line, "%d", &picked);
int cnum = sscanf(line, "%c", &c);
if (strlen(line) == 0 || dnum) {
--picked;
if (picked < nind){
int index = indexes[picked];
row = index / 19;
col = index % 19;
board[row*19 + col] = 1;
}
} else if (cnum){
if (c <= 'T' && c >= 'A'){
int num = sscanf(line, "%c %d", &c, &row);
row = (inverted)?19 - row : row-1;
col = c - 'A';
if (col > 7 && noi) col -= 1;
if (num == 2) board[row*19 + col] = 1;
} else if (c == 'p') {
// Pass
} else if(c=='b' || c == 'w'){
char g;
int num = sscanf(line, "%c %c %d", &g, &c, &row);
row = (inverted)?19 - row : row-1;
col = c - 'A';
if (col > 7 && noi) col -= 1;
if (num == 3) board[row*19 + col] = (g == 'b') ? color : -color;
} else if(c == 'c'){
char g;
int num = sscanf(line, "%c %c %d", &g, &c, &row);
row = (inverted)?19 - row : row-1;
col = c - 'A';
if (col > 7 && noi) col -= 1;
if (num == 3) board[row*19 + col] = 0;
}
}
free(line);
update_board(board);
flip_board(board);
color = -color;
}
}
void train_go(char *cfgfile, char *weightfile)
{
data_seed = time(0);
srand(time(0));
float avg_loss = -1;
char *base = basecfg(cfgfile);
printf("%s\n", base);
network net = parse_network_cfg(cfgfile);
if(weightfile){
load_weights(&net, weightfile);
}
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
char *backup_directory = "/home/pjreddie/backup/";
char buff[256];
sprintf(buff, "/home/pjreddie/go.train.%02d", rand()%10);
data train = load_go(buff);
int N = train.X.rows;
int epoch = (*net.seen)/N;
while(get_current_batch(net) < net.max_batches || net.max_batches == 0){
clock_t time=clock();
data batch = get_random_data(train, net.batch);
int i;
for(i = 0; i < batch.X.rows; ++i){
int flip = rand()%2;
int rotate = rand()%4;
image in = float_to_image(19, 19, 1, batch.X.vals[i]);
image out = float_to_image(19, 19, 1, batch.y.vals[i]);
//show_image_normalized(in, "in");
//show_image_normalized(out, "out");
if(flip){
flip_image(in);
flip_image(out);
}
rotate_image_cw(in, rotate);
rotate_image_cw(out, rotate);
//show_image_normalized(in, "in2");
//show_image_normalized(out, "out2");
//cvWaitKey(0);
}
float loss = train_network(net, batch);
free_data(batch);
if(avg_loss == -1) avg_loss = loss;
avg_loss = avg_loss*.95 + loss*.05;
printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen);
if(*net.seen/N > epoch){
epoch = *net.seen/N;
char buff[256];
sprintf(buff, "%s/%s_%d.weights",backup_directory,base, epoch);
save_weights(net, buff);
free_data(train);
sprintf(buff, "/home/pjreddie/go.train.%02d", epoch%10);
train = load_go(buff);
}
if(get_current_batch(net)%100 == 0){
char buff[256];
sprintf(buff, "%s/%s.backup",backup_directory,base);
save_weights(net, buff);
}
}
sprintf(buff, "%s/%s.weights", backup_directory, base);
save_weights(net, buff);
free_network(net);
free(base);
free_data(train);
}
