| | |
| | | load_weights(&net, weightfile); |
| | | } |
| | | printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); |
| | | //net.seen=0; |
| | | int imgs = 1024; |
| | | char **labels = get_labels("data/inet.labels.list"); |
| | | list *plist = get_paths("/data/imagenet/cls.train.list"); |
| | |
| | | args.type = CLASSIFICATION_DATA; |
| | | |
| | | load_thread = load_data_in_thread(args); |
| | | int epoch = net.seen/N; |
| | | while(1){ |
| | | int epoch = (*net.seen)/N; |
| | | while(get_current_batch(net) < net.max_batches || net.max_batches == 0){ |
| | | time=clock(); |
| | | pthread_join(load_thread, 0); |
| | | train = buffer; |
| | |
| | | printf("Loaded: %lf seconds\n", sec(clock()-time)); |
| | | time=clock(); |
| | | float loss = train_network(net, train); |
| | | net.seen += imgs; |
| | | if(avg_loss == -1) avg_loss = loss; |
| | | avg_loss = avg_loss*.9 + loss*.1; |
| | | printf("%.3f: %f, %f avg, %lf seconds, %d images\n", (float)net.seen/N, loss, avg_loss, sec(clock()-time), net.seen); |
| | | 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); |
| | | free_data(train); |
| | | if(net.seen/N > epoch){ |
| | | epoch = net.seen/N; |
| | | 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(epoch%22 == 0) net.learning_rate *= .1; |
| | | } |
| | | } |
| | | char buff[256]; |
| | | sprintf(buff, "%s/%s.weights", backup_directory, base); |
| | | save_weights(net, buff); |
| | | |
| | | pthread_join(load_thread, 0); |
| | | free_data(buffer); |
| | | free_network(net); |