| | |
| | | |
| | | void train_imagenet(char *cfgfile, char *weightfile) |
| | | { |
| | | float avg_loss = -1; |
| | | 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); |
| | |
| | | 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; |
| | | int i = net.seen/imgs; |
| | | char **labels = get_labels("/home/pjreddie/data/imagenet/cls.labels.list"); |
| | |
| | | avg_loss = avg_loss*.9 + loss*.1; |
| | | printf("%d: %f, %f avg, %lf seconds, %d images\n", i, loss, avg_loss, sec(clock()-time), net.seen); |
| | | free_data(train); |
| | | if((i % 15000) == 0) net.learning_rate *= .1; |
| | | //if(i%100 == 0 && net.learning_rate > .00001) net.learning_rate *= .97; |
| | | if(i%100==0){ |
| | | if(i%1000==0){ |
| | | char buff[256]; |
| | | sprintf(buff, "/home/pjreddie/imagenet_backup/%s_%d.weights",base, i); |
| | | save_weights(net, buff); |
| | |
| | | fgets(filename, 256, stdin); |
| | | strtok(filename, "\n"); |
| | | image im = load_image_color(filename, 256, 256); |
| | | translate_image(im, -128); |
| | | scale_image(im, 1/128.); |
| | | scale_image(im, 2.); |
| | | translate_image(im, -1.); |
| | | printf("%d %d %d\n", im.h, im.w, im.c); |
| | | float *X = im.data; |
| | | time=clock(); |