void test_cifar_csvtrain(char *filename, char *weightfile)
{
network net = parse_network_cfg(filename);
if(weightfile){
load_weights(&net, weightfile);
}
srand(time(0));
data test = load_all_cifar10();
matrix pred = network_predict_data(net, test);
int i;
for(i = 0; i < test.X.rows; ++i){
image im = float_to_image(32, 32, 3, test.X.vals[i]);
flip_image(im);
}
matrix pred2 = network_predict_data(net, test);
scale_matrix(pred, .5);
scale_matrix(pred2, .5);
matrix_add_matrix(pred2, pred);
matrix_to_csv(pred);
fprintf(stderr, "Accuracy: %f\n", matrix_topk_accuracy(test.y, pred, 1));
free_data(test);
}
void test_mnist_csv(char *filename, char *weightfile)
{
network net = parse_network_cfg(filename);
if(weightfile){
load_weights(&net, weightfile);
}
srand(time(0));
data test;
test = load_mnist_data("data/mnist/t10k-images.idx3-ubyte", "data/mnist/t10k-labels.idx1-ubyte", 10000);
matrix pred = network_predict_data(net, test);
int i;
for(i = 0; i < test.X.rows; ++i){
image im = float_to_image(32, 32, 3, test.X.vals[i]);
flip_image(im);
}
matrix pred2 = network_predict_data(net, test);
scale_matrix(pred, .5);
scale_matrix(pred2, .5);
matrix_add_matrix(pred2, pred);
matrix_to_csv(pred);
fprintf(stderr, "Accuracy: %f\n", matrix_topk_accuracy(test.y, pred, 1));
free_data(test);
}
void train_mnist_distill(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 = "backup";
int classes = 10;
int N = 50000;
int epoch = (*net.seen)/N;
data train;// = load_all_mnist10();
matrix soft = csv_to_matrix("results/ensemble.csv");
float weight = .9;
scale_matrix(soft, weight);
scale_matrix(train.y, 1. - weight);
matrix_add_matrix(soft, train.y);
while(get_current_batch(net) < net.max_batches || net.max_batches == 0){
clock_t time=clock();
float loss = train_network_sgd(net, train, 1);
if(avg_loss == -1) avg_loss = loss;
avg_loss = avg_loss*.95 + loss*.05;
if(get_current_batch(net)%100 == 0)
{
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);
}
if(get_current_batch(net)%100 == 0){
char buff[256];
sprintf(buff, "%s/%s.backup",backup_directory,base);
save_weights(net, buff);
}
}
char buff[256];
sprintf(buff, "%s/%s.weights", backup_directory, base);
save_weights(net, buff);
free_network(net);
free(base);
free_data(train);
}
