class torch.nn.PoissonNLLLoss(log_input: bool = True, full: bool = False, size_average=None, eps: float = 1e-08, reduce=None, reduction: str = 'mean')
[source]
Negative log likelihood loss with Poisson distribution of target.
The loss can be described as:
The last term can be omitted or approximated with Stirling formula. The approximation is used for target values more than 1. For targets less or equal to 1 zeros are added to the loss.
True
the loss is computed as $\exp(\text{input}) - \text{target}*\text{input}$ , if False
the loss is $\text{input} - \text{target}*\log(\text{input}+\text{eps})$ .full (bool, optional) –
whether to compute full loss, i. e. to add the Stirling approximation term
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
log_input = False
. Default: 1e-8reduction
). 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'
Examples:
>>> loss = nn.PoissonNLLLoss() >>> log_input = torch.randn(5, 2, requires_grad=True) >>> target = torch.randn(5, 2) >>> output = loss(log_input, target) >>> output.backward()
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.PoissonNLLLoss.html