class torch.nn.KLDivLoss(size_average=None, reduce=None, reduction: str = 'mean', log_target: bool = False)
[source]
The Kullback-Leibler divergence loss measure
Kullback-Leibler divergence is a useful distance measure for continuous distributions and is often useful when performing direct regression over the space of (discretely sampled) continuous output distributions.
As with NLLLoss
, the input
given is expected to contain log-probabilities and is not restricted to a 2D Tensor. The targets are interpreted as probabilities by default, but could be considered as log-probabilities with log_target
set to True
.
This criterion expects a target
Tensor
of the same size as the input
Tensor
.
The unreduced (i.e. with reduction
set to 'none'
) loss can be described as:
where the index $N$ spans all dimensions of input
and $L$ has the same shape as input
. If reduction
is not 'none'
(default 'mean'
), then:
In default reduction
mode 'mean'
, the losses are averaged for each minibatch over observations as well as over dimensions. 'batchmean'
mode gives the correct KL divergence where losses are averaged over batch dimension only. 'mean'
mode’s behavior will be changed to the same as 'batchmean'
in the next major release.
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'
| 'batchmean'
| 'sum'
| 'mean'
. 'none'
: no reduction will be applied. 'batchmean'
: the sum of the output will be divided by batchsize. 'sum'
: the output will be summed. 'mean'
: the output will be divided by the number of elements in the output. Default: 'mean'
target
is passed in the log space. Default: False
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
.
Note
reduction
= 'mean'
doesn’t return the true kl divergence value, please use reduction
= 'batchmean'
which aligns with KL math definition. In the next major release, 'mean'
will be changed to be the same as 'batchmean'
.
reduction
is 'none'
, then $(N, *)$ , the same shape as the input
© 2019 Torch Contributors
Licensed under the 3-clause BSD License.
https://pytorch.org/docs/1.7.0/generated/torch.nn.KLDivLoss.html