a15b3ec9a18377bf67356a9b5c29f4b767001d05,layers/losses.py,L1LossMasked,forward,#L1LossMasked#Any#Any#Any#,11

Before Change

        // mask: (batch, max_len, 1)
        mask = sequence_mask(
            sequence_length=length, max_len=target.size(1)).unsqueeze(2)
        losses = losses * mask.float()
        loss = losses.sum() / (length.float().sum() * float(target.shape[2]))
        return loss


class MSELossMasked(nn.Module):

After Change

        mask = mask.expand_as(input)
        loss = functional.l1_loss(
            input * mask, target * mask, reduction="sum")
        loss = loss / mask.sum()
        return loss


class MSELossMasked(nn.Module):

Instances