| | |
| | | } |
| | | if(clear) *net.seen = 0; |
| | | printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); |
| | | int imgs = net.batch; |
| | | int imgs = net.batch*net.subdivisions; |
| | | |
| | | list *options = read_data_cfg(datacfg); |
| | | |
| | |
| | | { |
| | | int i, j; |
| | | network net = parse_network_cfg(filename); |
| | | set_batch_network(&net, 1); |
| | | if(weightfile){ |
| | | load_weights(&net, weightfile); |
| | | } |
| | | set_batch_network(&net, 1); |
| | | srand(time(0)); |
| | | |
| | | list *options = read_data_cfg(datacfg); |
| | |
| | | int *indexes = calloc(top, sizeof(int)); |
| | | char buff[256]; |
| | | char *input = buff; |
| | | int size = net.w; |
| | | while(1){ |
| | | if(filename){ |
| | | strncpy(input, filename, 256); |
| | |
| | | if(!input) return; |
| | | strtok(input, "\n"); |
| | | } |
| | | image im = load_image_color(input, net.w, net.h); |
| | | float *X = im.data; |
| | | image im = load_image_color(input, 0, 0); |
| | | image r = resize_min(im, size); |
| | | resize_network(&net, r.w, r.h); |
| | | printf("%d %d\n", r.w, r.h); |
| | | |
| | | float *X = r.data; |
| | | time=clock(); |
| | | float *predictions = network_predict(net, X); |
| | | top_predictions(net, top, indexes); |
| | |
| | | int index = indexes[i]; |
| | | printf("%s: %f\n", names[index], predictions[index]); |
| | | } |
| | | if(r.data != im.data) free_image(r); |
| | | free_image(im); |
| | | if (filename) break; |
| | | } |