| | |
| | | //network net = parse_network_cfg("/home/pjreddie/imagenet_backup/alexnet_1270.cfg"); |
| | | srand(time(0)); |
| | | network net = parse_network_cfg(cfgfile); |
| | | set_learning_network(&net, .000001, .9, .0005); |
| | | set_learning_network(&net, net.learning_rate, .5, .0005); |
| | | printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); |
| | | int imgs = 1024; |
| | | int i = 20590; |
| | | int i = 23030; |
| | | char **labels = get_labels("/home/pjreddie/data/imagenet/cls.labels.list"); |
| | | list *plist = get_paths("/data/imagenet/cls.train.list"); |
| | | char **paths = (char **)list_to_array(plist); |
| | |
| | | avg_loss = avg_loss*.9 + loss*.1; |
| | | printf("%d: %f, %f avg, %lf seconds, %d images\n", i, loss, avg_loss, sec(clock()-time), i*imgs); |
| | | free_data(train); |
| | | if(i%10==0){ |
| | | if(i%100==0){ |
| | | char buff[256]; |
| | | sprintf(buff, "/home/pjreddie/imagenet_backup/net_%d.cfg", i); |
| | | save_network(net, buff); |
| | |
| | | void train_nist() |
| | | { |
| | | srand(222222); |
| | | network net = parse_network_cfg("cfg/nist.cfg"); |
| | | network net = parse_network_cfg("cfg/nist.cfg.old"); |
| | | data train = load_categorical_data_csv("data/mnist/mnist_train.csv", 0, 10); |
| | | data test = load_categorical_data_csv("data/mnist/mnist_test.csv",0,10); |
| | | normalize_data_rows(train); |
| | | normalize_data_rows(test); |
| | | int count = 0; |
| | | int iters = 60000/net.batch + 1; |
| | | while(++count <= 2000){ |
| | | while(++count <= 200){ |
| | | clock_t start = clock(), end; |
| | | float loss = train_network_sgd(net, train, iters); |
| | | end = clock(); |
| | |
| | | if(count%1 == 0) test_acc = network_accuracy(net, test); |
| | | printf("%d: Loss: %f, Test Acc: %f, Time: %lf seconds\n", count, loss, test_acc,(float)(end-start)/CLOCKS_PER_SEC); |
| | | } |
| | | save_network(net, "~/nist_conv.cfg"); |
| | | } |
| | | |
| | | void train_nist_distributed(char *address) |