void train_char_rnn(char *cfgfile, char *weightfile, char *filename, int clear, int tokenized)
{
srand(time(0));
data_seed = time(0);
unsigned char *text = 0;
int *tokens = 0;
size_t size;
if(tokenized){
tokens = read_tokenized_data(filename, &size);
} else {
FILE *fp = fopen(filename, "rb");
fseek(fp, 0, SEEK_END);
size = ftell(fp);
fseek(fp, 0, SEEK_SET);
text = calloc(size+1, sizeof(char));
fread(text, 1, size, fp);
fclose(fp);
}
char *backup_directory = "/home/pjreddie/backup/";
char *base = basecfg(cfgfile);
fprintf(stderr, "%s\n", base);
float avg_loss = -1;
network net = parse_network_cfg(cfgfile);
if(weightfile){
load_weights(&net, weightfile);
}
int inputs = get_network_input_size(net);
fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
int batch = net.batch;
int steps = net.time_steps;
if(clear) *net.seen = 0;
int i = (*net.seen)/net.batch;
int streams = batch/steps;
size_t *offsets = calloc(streams, sizeof(size_t));
int j;
for(j = 0; j < streams; ++j){
offsets[j] = rand_size_t()%size;
}
clock_t time;
while(get_current_batch(net) < net.max_batches){
i += 1;
time=clock();
float_pair p;
if(tokenized){
p = get_rnn_token_data(tokens, offsets, inputs, size, streams, steps);
}else{
p = get_rnn_data(text, offsets, inputs, size, streams, steps);
}
float loss = train_network_datum(net, p.x, p.y) / (batch);
free(p.x);
free(p.y);
if (avg_loss < 0) avg_loss = loss;
avg_loss = avg_loss*.9 + loss*.1;
int chars = get_current_batch(net)*batch;
fprintf(stderr, "%d: %f, %f avg, %f rate, %lf seconds, %f epochs\n", i, loss, avg_loss, get_current_rate(net), sec(clock()-time), (float) chars/size);
for(j = 0; j < streams; ++j){
//printf("%d\n", j);
if(rand()%10 == 0){
//fprintf(stderr, "Reset\n");
offsets[j] = rand_size_t()%size;
reset_rnn_state(net, j);
}
}
if(i%1000==0){
char buff[256];
sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);
save_weights(net, buff);
}
if(i%10==0){
char buff[256];
sprintf(buff, "%s/%s.backup", backup_directory, base);
save_weights(net, buff);
}
}
char buff[256];
sprintf(buff, "%s/%s_final.weights", backup_directory, base);
save_weights(net, buff);
}
void train_char_rnn(char *cfgfile, char *weightfile, char *filename, int clear,
int tokenized) {
srand(time(0));
unsigned char *text = 0;
int *tokens = 0;
size_t size;
if (tokenized) {
tokens = read_tokenized_data(filename, &size);
} else {
text = read_file(filename);
size = strlen((const char*) text);
}
char *backup_directory = "/home/pjreddie/backup/";
char *base = basecfg(cfgfile);
fprintf(stderr, "%s\n", base);
real_t avg_loss = -1;
network *net = load_network(cfgfile, weightfile, clear);
int inputs = net->inputs;
fprintf(stderr,
"Learning Rate: %g, Momentum: %g, Decay: %g, Inputs: %d %d %d\n",
net->learning_rate, net->momentum, net->decay, inputs, net->batch,
net->time_steps);
int batch = net->batch;
int steps = net->time_steps;
if (clear)
*net->seen = 0;
int i = (*net->seen) / net->batch;
int streams = batch / steps;
size_t *offsets = calloc(streams, sizeof(size_t));
int j;
for (j = 0; j < streams; ++j) {
offsets[j] = rand_size_t() % size;
}
clock_t time;
while (get_current_batch(net) < net->max_batches) {
i += 1;
time = clock();
real_t_pair p;
if (tokenized) {
p = get_rnn_token_data(tokens, offsets, inputs, size, streams,
steps);
} else {
p = get_rnn_data(text, offsets, inputs, size, streams, steps);
}
copy_cpu(net->inputs * net->batch, p.x, 1, net->input, 1);
copy_cpu(net->truths * net->batch, p.y, 1, net->truth, 1);
real_t loss = train_network_datum(net) / (batch);
free(p.x);
free(p.y);
if (avg_loss < 0)
avg_loss = loss;
avg_loss = avg_loss * .9 + loss * .1;
size_t chars = get_current_batch(net) * batch;
fprintf(stderr, "%d: %f, %f avg, %f rate, %lf seconds, %f epochs\n", i,
loss, avg_loss, get_current_rate(net), sec(clock() - time),
(real_t) chars / size);
for (j = 0; j < streams; ++j) {
//printf("%d\n", j);
if (rand() % 64 == 0) {
//fprintf(stderr, "Reset\n");
offsets[j] = rand_size_t() % size;
reset_network_state(net, j);
}
}
if (i % 10000 == 0) {
char buff[256];
sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);
save_weights(net, buff);
}
if (i % 100 == 0) {
char buff[256];
sprintf(buff, "%s/%s.backup", backup_directory, base);
save_weights(net, buff);
}
}
char buff[256];
sprintf(buff, "%s/%s_final.weights", backup_directory, base);
save_weights(net, buff);
}
