| | |
| | | |
| | | int ti = index + class_id; |
| | | float pt = output[ti] + 0.000000000000001F; |
| | | //float grad = -(1 - pt) * (2 * pt*logf(pt) + pt - 1); // http://blog.csdn.net/linmingan/article/details/77885832 |
| | | float grad = (1 - pt) * (2 * pt*logf(pt) + pt - 1); // https://github.com/unsky/focal-loss |
| | | // http://fooplot.com/#W3sidHlwZSI6MCwiZXEiOiItKDEteCkqKDIqeCpsb2coeCkreC0xKSIsImNvbG9yIjoiIzAwMDAwMCJ9LHsidHlwZSI6MTAwMH1d |
| | | float grad = -(1 - pt) * (2 * pt*logf(pt) + pt - 1); // http://blog.csdn.net/linmingan/article/details/77885832 |
| | | //float grad = (1 - pt) * (2 * pt*logf(pt) + pt - 1); // https://github.com/unsky/focal-loss |
| | | |
| | | for (n = 0; n < classes; ++n) { |
| | | delta[index + n] = scale * (((n == class_id) ? 1 : 0) - output[index + n]); |
| | |
| | | int class_index = entry_index(l, 0, n*l.w*l.h + i, l.coords + !l.background); |
| | | if (l.softmax_tree) { |
| | | |
| | | hierarchy_predictions(predictions + class_index, l.classes, l.softmax_tree, 0, l.w*l.h); |
| | | hierarchy_predictions(predictions + class_index, l.classes, l.softmax_tree, 0);// , l.w*l.h); |
| | | if (map) { |
| | | for (j = 0; j < 200; ++j) { |
| | | int class_index = entry_index(l, 0, n*l.w*l.h + i, l.coords + 1 + map[j]); |