| | |
| | | printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); |
| | | int imgs = 1024; |
| | | char **labels = get_labels("data/inet.labels.list"); |
| | | list *plist = get_paths("/data/imagenet/cls.train.list"); |
| | | list *plist = get_paths("data/inet.train.list"); |
| | | char **paths = (char **)list_to_array(plist); |
| | | printf("%d\n", plist->size); |
| | | int N = plist->size; |
| | |
| | | args.m = N; |
| | | args.labels = labels; |
| | | args.d = &buffer; |
| | | args.type = CLASSIFICATION_DATA; |
| | | args.type = OLD_CLASSIFICATION_DATA; |
| | | |
| | | load_thread = load_data_in_thread(args); |
| | | int epoch = (*net.seen)/N; |
| | |
| | | pthread_join(load_thread, 0); |
| | | train = buffer; |
| | | |
| | | /* |
| | | image im = float_to_image(256, 256, 3, train.X.vals[114]); |
| | | show_image(im, "training"); |
| | | cvWaitKey(0); |
| | | */ |
| | | |
| | | load_thread = load_data_in_thread(args); |
| | | printf("Loaded: %lf seconds\n", sec(clock()-time)); |
| | | time=clock(); |
| | |
| | | sprintf(buff, "%s/%s_%d.weights",backup_directory,base, epoch); |
| | | save_weights(net, buff); |
| | | } |
| | | if(*net.seen%1000 == 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); |
| | |
| | | srand(time(0)); |
| | | |
| | | char **labels = get_labels("data/inet.labels.list"); |
| | | //list *plist = get_paths("data/inet.suppress.list"); |
| | | list *plist = get_paths("data/inet.val.list"); |
| | | |
| | | char **paths = (char **)list_to_array(plist); |
| | |
| | | args.m = 0; |
| | | args.labels = labels; |
| | | args.d = &buffer; |
| | | args.type = CLASSIFICATION_DATA; |
| | | args.type = OLD_CLASSIFICATION_DATA; |
| | | |
| | | pthread_t load_thread = load_data_in_thread(args); |
| | | for(i = 1; i <= splits; ++i){ |