a807f6de32213cfec9462c8df2ca1fad4f9bcbad,python/mxnet/gluon/loss.py,L1Loss,hybrid_forward,#L1Loss#Any#Any#Any#Any#,189

Before Change

        label = _reshape_like(F, label, pred)
        loss = F.np.abs(label - pred) if is_np_array() else F.abs(label - pred)
        loss = _apply_weighting(F, loss, self._weight, sample_weight)
        if is_np_array():
            if F is ndarray:
                return F.np.mean(loss, axis=tuple(range(1, loss.ndim)))
            else:
                return F.npx.batch_flatten(loss).mean(axis=1)
        else:
            return F.mean(loss, axis=self._batch_axis, exclude=True)


class SigmoidBinaryCrossEntropyLoss(Loss):
    rThe cross-entropy loss for binary classification. (alias: SigmoidBCELoss)

    BCE loss is useful when training logistic regression. If `from_sigmoid`

After Change

        super(L1Loss, self).__init__(weight, batch_axis, **kwargs)

    def hybrid_forward(self, F, pred, label, sample_weight=None):
        abs_fn = F.np.abs if is_np_array() else F.abs
        label = _reshape_like(F, label, pred)
        loss = abs_fn(label - pred)
        loss = _apply_weighting(F, loss, self._weight, sample_weight)

Instances