- class torch.nn.PoissonNLLLoss(log_input=True, full=False, size_average=None, eps=1e-08, reduce=None, reduction='mean')¶
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.
log_input (bool, optional) – if
Truethe loss is computed as , if
Falsethe loss is .
full (bool, optional) –
whether to compute full loss, i. e. to add the Stirling approximation term
size_average (bool, optional) – Deprecated (see
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_averageis set to
False, the losses are instead summed for each minibatch. Ignored when
eps (float, optional) – Small value to avoid evaluation of when
log_input = False. Default: 1e-8
reduce (bool, optional) – Deprecated (see
reduction). By default, the losses are averaged or summed over observations for each minibatch depending on
False, returns a loss per batch element instead and ignores
reduction (str, optional) – Specifies the reduction to apply to the output:
'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:
reduceare in the process of being deprecated, and in the meantime, specifying either of those two args will override
>>> loss = nn.PoissonNLLLoss() >>> log_input = torch.randn(5, 2, requires_grad=True) >>> target = torch.randn(5, 2) >>> output = loss(log_input, target) >>> output.backward()
Input: , where means any number of dimensions.
Target: , same shape as the input.
Output: scalar by default. If
'none', then , the same shape as the input.