void valid_char_rnn(char *cfgfile, char *weightfile)
{
char *base = basecfg(cfgfile);
fprintf(stderr, "%s\n", base);
network net = parse_network_cfg(cfgfile);
if(weightfile){
load_weights(&net, weightfile);
}
int inputs = get_network_input_size(net);
int count = 0;
int c;
float *input = calloc(inputs, sizeof(float));
float sum = 0;
c = getc(stdin);
float log2 = log(2);
while(c != EOF){
int next = getc(stdin);
if(next == EOF) break;
++count;
input[c] = 1;
float *out = network_predict(net, input);
input[c] = 0;
sum += log(out[next])/log2;
c = next;
}
printf("Perplexity: %f\n", pow(2, -sum/count));
}
void test_char_rnn(char *cfgfile, char *weightfile, int num, char *seed, float 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 = parse_network_cfg(cfgfile);
if(weightfile){
load_weights(&net, weightfile);
}
int inputs = get_network_input_size(net);
int i, j;
for(i = 0; i < net.n; ++i) net.layers[i].temperature = temp;
int c = 0;
int len = strlen(seed);
float *input = calloc(inputs, sizeof(float));
/*
fill_cpu(inputs, 0, input, 1);
for(i = 0; i < 10; ++i){
network_predict(net, input);
}
fill_cpu(inputs, 0, input, 1);
*/
for(i = 0; i < len-1; ++i){
c = seed[i];
input[c] = 1;
network_predict(net, input);
input[c] = 0;
print_symbol(c, tokens);
}
if(len) c = seed[len-1];
print_symbol(c, tokens);
for(i = 0; i < num; ++i){
input[c] = 1;
float *out = network_predict(net, input);
input[c] = 0;
for(j = 32; j < 127; ++j){
//printf("%d %c %f\n",j, j, out[j]);
}
for(j = 0; j < inputs; ++j){
if (out[j] < .0001) out[j] = 0;
}
c = sample_array(out, inputs);
print_symbol(c, tokens);
}
printf("\n");
}
void train_char_rnn(char *cfgfile, char *weightfile, char *filename)
{
FILE *fp = fopen(filename, "rb");
fseek(fp, 0, SEEK_END);
size_t size = ftell(fp);
fseek(fp, 0, SEEK_SET);
unsigned char *text = calloc(size+1, sizeof(char));
fread(text, 1, size, fp);
fclose(fp);
char *backup_directory = "/home/pjreddie/backup/";
srand(time(0));
data_seed = time(0);
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;
int i = (*net.seen)/net.batch;
clock_t time;
while(get_current_batch(net) < net.max_batches){
i += 1;
time=clock();
float_pair p = get_rnn_data(text, inputs, size, batch/steps, 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;
fprintf(stderr, "%d: %f, %f avg, %f rate, %lf seconds\n", i, loss, avg_loss, get_current_rate(net), sec(clock()-time));
if(i%100==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 valid_tactic_rnn(char *cfgfile, char *weightfile, char *seed)
{
char *base = basecfg(cfgfile);
fprintf(stderr, "%s\n", base);
network net = parse_network_cfg(cfgfile);
if(weightfile){
load_weights(&net, weightfile);
}
int inputs = get_network_input_size(net);
int count = 0;
int words = 1;
int c;
int len = strlen(seed);
float *input = calloc(inputs, sizeof(float));
int i;
for(i = 0; i < len; ++i){
c = seed[i];
input[(int)c] = 1;
network_predict(net, input);
input[(int)c] = 0;
}
float sum = 0;
c = getc(stdin);
float log2 = log(2);
int in = 0;
while(c != EOF){
int next = getc(stdin);
if(next == EOF) break;
if(next < 0 || next >= 255) error("Out of range character");
input[c] = 1;
float *out = network_predict(net, input);
input[c] = 0;
if(c == '.' && next == '\n') in = 0;
if(!in) {
if(c == '>' && next == '>'){
in = 1;
++words;
}
c = next;
continue;
}
++count;
sum += log(out[next])/log2;
c = next;
printf("%d %d Perplexity: %4.4f Word Perplexity: %4.4f\n", count, words, pow(2, -sum/count), pow(2, -sum/words));
}
}
void vec_char_rnn(char *cfgfile, char *weightfile, char *seed)
{
char *base = basecfg(cfgfile);
fprintf(stderr, "%s\n", base);
network net = parse_network_cfg(cfgfile);
if(weightfile){
load_weights(&net, weightfile);
}
int inputs = get_network_input_size(net);
int c;
int seed_len = strlen(seed);
float *input = calloc(inputs, sizeof(float));
int i;
char *line;
while((line=fgetl(stdin)) != 0){
reset_rnn_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");
}
}
std::string ofxDarknet::rnn(int num, std::string seed, float temp )
{
int inputs = get_network_input_size( net );
for( int i = 0; i < net.n; ++i )
{
net.layers[ i ].temperature = temp;
}
int c = 0;
int len = seed.length();
float *input = ( float* ) calloc( inputs, sizeof( float ) );
std::string sampled_text;
for( int i = 0; i < len - 1; ++i ) {
c = seed[ i ];
input[ c ] = 1;
network_predict( net, input );
input[ c ] = 0;
char _c = c;
sampled_text += _c;
}
if( len ) c = seed[ len - 1 ];
char _c = c;
sampled_text += _c;
for( int i = 0; i < num; ++i ) {
input[ c ] = 1;
float *out = network_predict( net, input );
input[ c ] = 0;
for( int j = 0; j < inputs; ++j ) {
if( out[ j ] < .0001 ) out[ j ] = 0;
}
c = sample_array( out, inputs );
char _c = c;
sampled_text += _c;
}
delete input;
return sampled_text;
}
void test_tactic_rnn(char *cfgfile, char *weightfile, int num, float 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 = parse_network_cfg(cfgfile);
if(weightfile){
load_weights(&net, weightfile);
}
int inputs = get_network_input_size(net);
int i, j;
for(i = 0; i < net.n; ++i) net.layers[i].temperature = temp;
int c = 0;
float *input = calloc(inputs, sizeof(float));
float *out = 0;
while((c = getc(stdin)) != EOF){
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 test_char_rnn(char *cfgfile, char *weightfile, int num, char *seed, float temp, int rseed)
{
srand(rseed);
char *base = basecfg(cfgfile);
fprintf(stderr, "%s\n", base);
network net = parse_network_cfg(cfgfile);
if(weightfile){
load_weights(&net, weightfile);
}
int inputs = get_network_input_size(net);
int i, j;
for(i = 0; i < net.n; ++i) net.layers[i].temperature = temp;
unsigned char c;
int len = strlen(seed);
float *input = calloc(inputs, sizeof(float));
for(i = 0; i < len-1; ++i){
c = seed[i];
input[(int)c] = 1;
network_predict(net, input);
input[(int)c] = 0;
printf("%c", c);
}
c = seed[len-1];
for(i = 0; i < num; ++i){
printf("%c", c);
float r = rand_uniform(0,1);
float sum = 0;
input[(int)c] = 1;
float *out = network_predict(net, input);
input[(int)c] = 0;
for(j = 0; j < inputs; ++j){
sum += out[j];
if(sum > r) break;
}
c = j;
}
printf("\n");
}
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_lsd2(char *cfgfile, char *weightfile, char *acfgfile, char *aweightfile, int clear)
{
#ifdef GPU
char *train_images = "/home/pjreddie/data/coco/trainvalno5k.txt";
char *backup_directory = "/home/pjreddie/backup/";
srand(time(0));
char *base = basecfg(cfgfile);
printf("%s\n", base);
network net = parse_network_cfg(cfgfile);
if(weightfile){
load_weights(&net, weightfile);
}
if(clear) *net.seen = 0;
char *abase = basecfg(acfgfile);
network anet = parse_network_cfg(acfgfile);
if(aweightfile){
load_weights(&anet, aweightfile);
}
if(clear) *anet.seen = 0;
int i, j, k;
layer imlayer = {};
for (i = 0; i < net.n; ++i) {
if (net.layers[i].out_c == 3) {
imlayer = net.layers[i];
break;
}
}
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
int imgs = net.batch*net.subdivisions;
i = *net.seen/imgs;
data train, buffer;
list *plist = get_paths(train_images);
//int N = plist->size;
char **paths = (char **)list_to_array(plist);
load_args args = {};
args.w = net.w;
args.h = net.h;
args.paths = paths;
args.n = imgs;
args.m = plist->size;
args.d = &buffer;
args.min = net.min_crop;
args.max = net.max_crop;
args.angle = net.angle;
args.aspect = net.aspect;
args.exposure = net.exposure;
args.saturation = net.saturation;
args.hue = net.hue;
args.size = net.w;
args.type = CLASSIFICATION_DATA;
args.classes = 1;
char *ls[1] = {"coco"};
args.labels = ls;
pthread_t load_thread = load_data_in_thread(args);
clock_t time;
network_state gstate = {};
gstate.index = 0;
gstate.net = net;
int x_size = get_network_input_size(net)*net.batch;
int y_size = 1*net.batch;
gstate.input = cuda_make_array(0, x_size);
gstate.truth = 0;
gstate.delta = 0;
gstate.train = 1;
float *X = (float*)calloc(x_size, sizeof(float));
float *y = (float*)calloc(y_size, sizeof(float));
network_state astate = {};
astate.index = 0;
astate.net = anet;
int ay_size = get_network_output_size(anet)*anet.batch;
astate.input = 0;
astate.truth = 0;
astate.delta = 0;
astate.train = 1;
float *imerror = cuda_make_array(0, imlayer.outputs);
float *ones_gpu = cuda_make_array(0, ay_size);
fill_ongpu(ay_size, 1, ones_gpu, 1);
float aloss_avg = -1;
float gloss_avg = -1;
//data generated = copy_data(train);
while (get_current_batch(net) < net.max_batches) {
i += 1;
time=clock();
pthread_join(load_thread, 0);
train = buffer;
load_thread = load_data_in_thread(args);
printf("Loaded: %lf seconds\n", sec(clock()-time));
data generated = copy_data(train);
time=clock();
float gloss = 0;
for(j = 0; j < net.subdivisions; ++j){
get_next_batch(train, net.batch, j*net.batch, X, y);
cuda_push_array(gstate.input, X, x_size);
*net.seen += net.batch;
forward_network_gpu(net, gstate);
fill_ongpu(imlayer.outputs, 0, imerror, 1);
astate.input = imlayer.output_gpu;
astate.delta = imerror;
astate.truth = ones_gpu;
forward_network_gpu(anet, astate);
backward_network_gpu(anet, astate);
scal_ongpu(imlayer.outputs, 1, imerror, 1);
axpy_ongpu(imlayer.outputs, 1, imerror, 1, imlayer.delta_gpu, 1);
backward_network_gpu(net, gstate);
printf("features %f\n", cuda_mag_array(imlayer.delta_gpu, imlayer.outputs));
printf("realness %f\n", cuda_mag_array(imerror, imlayer.outputs));
gloss += get_network_cost(net) /(net.subdivisions*net.batch);
cuda_pull_array(imlayer.output_gpu, imlayer.output, x_size);
for(k = 0; k < net.batch; ++k){
int index = j*net.batch + k;
copy_cpu(imlayer.outputs, imlayer.output + k*imlayer.outputs, 1, generated.X.vals[index], 1);
generated.y.vals[index][0] = 0;
}
}
harmless_update_network_gpu(anet);
data merge = concat_data(train, generated);
randomize_data(merge);
float aloss = train_network(anet, merge);
update_network_gpu(net);
update_network_gpu(anet);
free_data(merge);
free_data(train);
free_data(generated);
if (aloss_avg < 0) aloss_avg = aloss;
aloss_avg = aloss_avg*.9 + aloss*.1;
gloss_avg = gloss_avg*.9 + gloss*.1;
printf("%d: gen: %f, adv: %f | gen_avg: %f, adv_avg: %f, %f rate, %lf seconds, %d images\n", i, gloss, aloss, gloss_avg, aloss_avg, get_current_rate(net), sec(clock()-time), i*imgs);
if(i%1000==0){
char buff[256];
sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);
save_weights(net, buff);
sprintf(buff, "%s/%s_%d.weights", backup_directory, abase, i);
save_weights(anet, buff);
}
if(i%100==0){
char buff[256];
sprintf(buff, "%s/%s.backup", backup_directory, base);
save_weights(net, buff);
sprintf(buff, "%s/%s.backup", backup_directory, abase);
save_weights(anet, buff);
}
}
char buff[256];
sprintf(buff, "%s/%s_final.weights", backup_directory, base);
save_weights(net, buff);
#endif
}
void train_lsd3(char *fcfg, char *fweight, char *gcfg, char *gweight, char *acfg, char *aweight, int clear)
{
#ifdef GPU
//char *train_images = "/home/pjreddie/data/coco/trainvalno5k.txt";
char *train_images = "/home/pjreddie/data/imagenet/imagenet1k.train.list";
//char *style_images = "/home/pjreddie/data/coco/trainvalno5k.txt";
char *style_images = "/home/pjreddie/zelda.txt";
char *backup_directory = "/home/pjreddie/backup/";
srand(time(0));
network fnet = load_network(fcfg, fweight, clear);
network gnet = load_network(gcfg, gweight, clear);
network anet = load_network(acfg, aweight, clear);
char *gbase = basecfg(gcfg);
char *abase = basecfg(acfg);
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", gnet.learning_rate, gnet.momentum, gnet.decay);
int imgs = gnet.batch*gnet.subdivisions;
int i = *gnet.seen/imgs;
data train, tbuffer;
data style, sbuffer;
list *slist = get_paths(style_images);
char **spaths = (char **)list_to_array(slist);
list *tlist = get_paths(train_images);
char **tpaths = (char **)list_to_array(tlist);
load_args targs= get_base_args(gnet);
targs.paths = tpaths;
targs.n = imgs;
targs.m = tlist->size;
targs.d = &tbuffer;
targs.type = CLASSIFICATION_DATA;
targs.classes = 1;
char *ls[1] = {"zelda"};
targs.labels = ls;
load_args sargs = get_base_args(gnet);
sargs.paths = spaths;
sargs.n = imgs;
sargs.m = slist->size;
sargs.d = &sbuffer;
sargs.type = CLASSIFICATION_DATA;
sargs.classes = 1;
sargs.labels = ls;
pthread_t tload_thread = load_data_in_thread(targs);
pthread_t sload_thread = load_data_in_thread(sargs);
clock_t time;
float aloss_avg = -1;
float floss_avg = -1;
network_state fstate = {};
fstate.index = 0;
fstate.net = fnet;
int x_size = get_network_input_size(fnet)*fnet.batch;
int y_size = get_network_output_size(fnet)*fnet.batch;
fstate.input = cuda_make_array(0, x_size);
fstate.truth = cuda_make_array(0, y_size);
fstate.delta = cuda_make_array(0, x_size);
fstate.train = 1;
float *X = (float*)calloc(x_size, sizeof(float));
float *y = (float*)calloc(y_size, sizeof(float));
float *ones = cuda_make_array(0, anet.batch);
float *zeros = cuda_make_array(0, anet.batch);
fill_ongpu(anet.batch, .99, ones, 1);
fill_ongpu(anet.batch, .01, zeros, 1);
network_state astate = {};
astate.index = 0;
astate.net = anet;
int ax_size = get_network_input_size(anet)*anet.batch;
int ay_size = get_network_output_size(anet)*anet.batch;
astate.input = 0;
astate.truth = ones;
astate.delta = cuda_make_array(0, ax_size);
astate.train = 1;
network_state gstate = {};
gstate.index = 0;
gstate.net = gnet;
int gx_size = get_network_input_size(gnet)*gnet.batch;
int gy_size = get_network_output_size(gnet)*gnet.batch;
gstate.input = cuda_make_array(0, gx_size);
gstate.truth = 0;
gstate.delta = 0;
gstate.train = 1;
while (get_current_batch(gnet) < gnet.max_batches) {
i += 1;
time=clock();
pthread_join(tload_thread, 0);
pthread_join(sload_thread, 0);
train = tbuffer;
style = sbuffer;
tload_thread = load_data_in_thread(targs);
sload_thread = load_data_in_thread(sargs);
printf("Loaded: %lf seconds\n", sec(clock()-time));
data generated = copy_data(train);
time=clock();
int j, k;
float floss = 0;
for(j = 0; j < fnet.subdivisions; ++j){
layer imlayer = gnet.layers[gnet.n - 1];
get_next_batch(train, fnet.batch, j*fnet.batch, X, y);
cuda_push_array(fstate.input, X, x_size);
cuda_push_array(gstate.input, X, gx_size);
*gnet.seen += gnet.batch;
forward_network_gpu(fnet, fstate);
float *feats = fnet.layers[fnet.n - 2].output_gpu;
copy_ongpu(y_size, feats, 1, fstate.truth, 1);
forward_network_gpu(gnet, gstate);
float *gen = gnet.layers[gnet.n-1].output_gpu;
copy_ongpu(x_size, gen, 1, fstate.input, 1);
fill_ongpu(x_size, 0, fstate.delta, 1);
forward_network_gpu(fnet, fstate);
backward_network_gpu(fnet, fstate);
//HERE
astate.input = gen;
fill_ongpu(ax_size, 0, astate.delta, 1);
forward_network_gpu(anet, astate);
backward_network_gpu(anet, astate);
float *delta = imlayer.delta_gpu;
fill_ongpu(x_size, 0, delta, 1);
scal_ongpu(x_size, 100, astate.delta, 1);
scal_ongpu(x_size, .00001, fstate.delta, 1);
axpy_ongpu(x_size, 1, fstate.delta, 1, delta, 1);
axpy_ongpu(x_size, 1, astate.delta, 1, delta, 1);
//fill_ongpu(x_size, 0, delta, 1);
//cuda_push_array(delta, X, x_size);
//axpy_ongpu(x_size, -1, imlayer.output_gpu, 1, delta, 1);
//printf("pix error: %f\n", cuda_mag_array(delta, x_size));
printf("fea error: %f\n", cuda_mag_array(fstate.delta, x_size));
printf("adv error: %f\n", cuda_mag_array(astate.delta, x_size));
//axpy_ongpu(x_size, 1, astate.delta, 1, delta, 1);
backward_network_gpu(gnet, gstate);
floss += get_network_cost(fnet) /(fnet.subdivisions*fnet.batch);
cuda_pull_array(imlayer.output_gpu, imlayer.output, x_size);
for(k = 0; k < gnet.batch; ++k){
int index = j*gnet.batch + k;
copy_cpu(imlayer.outputs, imlayer.output + k*imlayer.outputs, 1, generated.X.vals[index], 1);
generated.y.vals[index][0] = .01;
}
}
/*
image sim = float_to_image(anet.w, anet.h, anet.c, style.X.vals[j]);
show_image(sim, "style");
cvWaitKey(0);
*/
harmless_update_network_gpu(anet);
data merge = concat_data(style, generated);
randomize_data(merge);
float aloss = train_network(anet, merge);
update_network_gpu(gnet);
free_data(merge);
free_data(train);
free_data(generated);
free_data(style);
if (aloss_avg < 0) aloss_avg = aloss;
if (floss_avg < 0) floss_avg = floss;
aloss_avg = aloss_avg*.9 + aloss*.1;
floss_avg = floss_avg*.9 + floss*.1;
printf("%d: gen: %f, adv: %f | gen_avg: %f, adv_avg: %f, %f rate, %lf seconds, %d images\n", i, floss, aloss, floss_avg, aloss_avg, get_current_rate(gnet), sec(clock()-time), i*imgs);
if(i%1000==0){
char buff[256];
sprintf(buff, "%s/%s_%d.weights", backup_directory, gbase, i);
save_weights(gnet, buff);
sprintf(buff, "%s/%s_%d.weights", backup_directory, abase, i);
save_weights(anet, buff);
}
if(i%100==0){
char buff[256];
sprintf(buff, "%s/%s.backup", backup_directory, gbase);
save_weights(gnet, buff);
sprintf(buff, "%s/%s.backup", backup_directory, abase);
save_weights(anet, buff);
}
}
#endif
}
