void test_tactic_rnn_multi(char *cfgfile, char *weightfile, int num,
real_t temp, int rseed, char *token_file) {
char **tokens = 0;
if (token_file) {
size_t n;
tokens = read_tokens(token_file, &n);
}
srand(rseed);
char *base = basecfg(cfgfile);
fprintf(stderr, "%s\n", base);
network *net = load_network(cfgfile, weightfile, 0);
int inputs = net->inputs;
int i, j;
for (i = 0; i < net->n; ++i)
net->layers[i].temperature = temp;
int c = 0;
real_t *input = calloc(inputs, sizeof(real_t));
real_t *out = 0;
while (1) {
reset_network_state(net, 0);
while ((c = getc(stdin)) != EOF && c != 0) {
input[c] = 1;
out = network_predict(net, input);
input[c] = 0;
}
for (i = 0; i < num; ++i) {
for (j = 0; j < inputs; ++j) {
if (out[j] < .0001)
out[j] = 0;
}
int next = sample_array(out, inputs);
if (c == '.' && next == '\n')
break;
c = next;
print_symbol(c, tokens);
input[c] = 1;
out = network_predict(net, input);
input[c] = 0;
}
printf("\n");
}
}
void vec_char_rnn(char *cfgfile, char *weightfile, char *seed) {
char *base = basecfg(cfgfile);
fprintf(stderr, "%s\n", base);
network *net = load_network(cfgfile, weightfile, 0);
int inputs = net->inputs;
int c;
int seed_len = strlen(seed);
real_t *input = calloc(inputs, sizeof(real_t));
int i;
char *line;
while ((line = fgetl(stdin)) != 0) {
reset_network_state(net, 0);
for (i = 0; i < seed_len; ++i) {
c = seed[i];
input[(int) c] = 1;
network_predict(net, input);
input[(int) c] = 0;
}
strip(line);
int str_len = strlen(line);
for (i = 0; i < str_len; ++i) {
c = line[i];
input[(int) c] = 1;
network_predict(net, input);
input[(int) c] = 0;
}
c = ' ';
input[(int) c] = 1;
network_predict(net, input);
input[(int) c] = 0;
layer l = net->layers[0];
#ifdef GPU
cuda_pull_array(l.output_gpu, l.output, l.outputs);
#endif
printf("%s", line);
for (i = 0; i < l.outputs; ++i) {
printf(",%g", l.output[i]);
}
printf("\n");
}
}
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);
}
void reset_rnn(network *net)
{
reset_network_state(net, 0);
}