3c761ba9d857677efb60ae5c2dbbe321f0b3b702,allennlp/nn/util.py,,last_dim_softmax,#Any#Any#,305

Before Change

    :func:`masked_softmax`, then put the tensor back in its original shape.
    
    tensor_shape = tensor.size()
    reshaped_tensor = tensor.view(-1, tensor.size()[-1])
    if mask is not None:
        while mask.dim() < tensor.dim():
            mask = mask.unsqueeze(1)
        mask = mask.expand_as(tensor).contiguous().float()
        mask = mask.view(-1, mask.size()[-1])
    reshaped_result = masked_softmax(reshaped_tensor, mask)
    return reshaped_result.view(*tensor_shape)


def weighted_sum(matrix: torch.Tensor, attention: torch.Tensor) -> torch.Tensor:
    

After Change

    assume the tensor has shape ``(batch_size, ..., sequence_length)`` and that the mask (if given)
    has shape ``(batch_size, sequence_length)``.
    
    return _last_dimension_applicator(masked_softmax, tensor, mask)


def last_dim_log_softmax(tensor: torch.Tensor, mask: Optional[torch.Tensor] = None) -> torch.Tensor:
    

Instances