| | |
| | | image *prev = 0; |
| | | int i; |
| | | char buff[256]; |
| | | show_image(get_network_image_layer(net, 0), "Crop"); |
| | | //show_image(get_network_image_layer(net, 0), "Crop"); |
| | | for(i = 0; i < net.n; ++i){ |
| | | sprintf(buff, "Layer %d", i); |
| | | if(net.types[i] == CONVOLUTIONAL){ |
| | |
| | | } |
| | | } |
| | | |
| | | void top_predictions(network net, int n, int *index) |
| | | { |
| | | int i,j; |
| | | int k = get_network_output_size(net); |
| | | float *out = get_network_output(net); |
| | | float thresh = FLT_MAX; |
| | | for(i = 0; i < n; ++i){ |
| | | float max = -FLT_MAX; |
| | | int max_i = -1; |
| | | for(j = 0; j < k; ++j){ |
| | | float val = out[j]; |
| | | if(val > max && val < thresh){ |
| | | max = val; |
| | | max_i = j; |
| | | } |
| | | } |
| | | index[i] = max_i; |
| | | thresh = max; |
| | | } |
| | | } |
| | | |
| | | float *network_predict(network net, float *input) |
| | | { |
| | | forward_network(net, input, 0, 0); |