| 53 | |
| 54 | |
| 55 | def accuracy_metric(y_pred, y_true, void_labels, one_hot=False): |
| 56 | |
| 57 | assert (y_pred.ndim == 2) or (y_pred.ndim == 1) |
| 58 | |
| 59 | # y_pred to indices |
| 60 | if y_pred.ndim == 2: |
| 61 | y_pred = T.argmax(y_pred, axis=1) |
| 62 | |
| 63 | if one_hot: |
| 64 | y_true = T.argmax(y_true, axis=1) |
| 65 | |
| 66 | # Compute accuracy |
| 67 | acc = T.eq(y_pred, y_true).astype(_FLOATX) |
| 68 | |
| 69 | # Create mask |
| 70 | mask = T.ones_like(y_true, dtype=_FLOATX) |
| 71 | for el in void_labels: |
| 72 | indices = T.eq(y_true, el).nonzero() |
| 73 | if any(indices): |
| 74 | mask = T.set_subtensor(mask[indices], 0.) |
| 75 | |
| 76 | # Apply mask |
| 77 | acc *= mask |
| 78 | acc = T.sum(acc) / T.sum(mask) |
| 79 | |
| 80 | return acc |
| 81 | |
| 82 | |
| 83 | def crossentropy_metric(y_pred, y_true, void_labels, one_hot=False): |