class torch.nn.MultiMarginLoss(p: int = 1, margin: float = 1.0, weight: Optional[torch.Tensor] = None, size_average=None, reduce=None, reduction: str = 'mean')
[source]
Creates a criterion that optimizes a multi-class classification hinge loss (margin-based loss) between input $x$ (a 2D mini-batch Tensor
) and output $y$ (which is a 1D tensor of target class indices, $0 \leq y \leq \text{x.size}(1)-1$ ):
For each mini-batch sample, the loss in terms of the 1D input $x$ and scalar output $y$ is:
where $x \in \left\{0, \; \cdots , \; \text{x.size}(0) - 1\right\}$ and $i \neq y$ .
Optionally, you can give non-equal weighting on the classes by passing a 1D weight
tensor into the constructor.
The loss function then becomes:
C
. Otherwise, it is treated as if having all ones.reduction
). By default, the losses are averaged over each loss element in the batch. Note that for some losses, there are multiple elements per sample. If the field size_average
is set to False
, the losses are instead summed for each minibatch. Ignored when reduce is False
. Default: True
reduction
). By default, the losses are averaged or summed over observations for each minibatch depending on size_average
. When reduce
is False
, returns a loss per batch element instead and ignores size_average
. Default: True
'none'
| 'mean'
| 'sum'
. 'none'
: no reduction will be applied, 'mean'
: the sum of the output will be divided by the number of elements in the output, 'sum'
: the output will be summed. Note: size_average
and reduce
are in the process of being deprecated, and in the meantime, specifying either of those two args will override reduction
. Default: 'mean'
© 2019 Torch Contributors
Licensed under the 3-clause BSD License.
https://pytorch.org/docs/1.7.0/generated/torch.nn.MultiMarginLoss.html