PoissonNLLLoss#

classtorch.nn.PoissonNLLLoss(log_input=True,full=False,size_average=None,eps=1e-08,reduce=None,reduction='mean')[source]#

Negative log likelihood loss with Poisson distribution of target.

The loss can be described as:

$$\text{target}\sim \mathrm{P}\mathrm{o}\mathrm{i}\mathrm{s}\mathrm{s}\mathrm{o}\mathrm{n}(\text{input})\text{loss}(\text{input},\text{target})=\text{input}-\text{target}\ast \log (\text{input})+\log (\text{target!})$$target∼Poisson(input)loss(input,target)=input−target∗log(input)+log(target!)

The last term can be omitted or approximated with Stirling formula. Theapproximation is used for target values more than 1. For targets less orequal to 1 zeros are added to the loss.

Parameters

• log_input (bool,optional) – ifTrue the loss is computed as$\exp (\text{input})-\text{target}\ast \text{input}$exp(input)−target∗input, ifFalse the loss is$\text{input}-\text{target}\ast \log (\text{input}+\text{eps})$input−target∗log(input+eps).

• full (bool,optional) –

  whether to compute full loss, i. e. to add theStirling approximation term

  $$\text{target}\ast \log (\text{target})-\text{target}+0.5\ast \log (2\pi \text{target})\mathrm{.}$$target∗log(target)−target+0.5∗log(2πtarget).

• size_average (bool,optional) – Deprecated (seereduction). By default,the losses are averaged over each loss element in the batch. Note that forsome losses, there are multiple elements per sample. If the fieldsize_averageis set toFalse, the losses are instead summed for each minibatch. Ignoredwhenreduce isFalse. Default:True

• eps (float,optional) – Small value to avoid evaluation of$\log (0)$log(0) whenlog_input=False. Default: 1e-8

• reduce (bool,optional) – Deprecated (seereduction). By default, thelosses are averaged or summed over observations for each minibatch dependingonsize_average. Whenreduce isFalse, returns a loss perbatch element instead and ignoressize_average. Default:True

• reduction (str,optional) – Specifies the reduction to apply to the output:'none' |'mean' |'sum'.'none': no reduction will be applied,'mean': the sum of the output will be divided by the number ofelements in the output,'sum': the output will be summed. Note:size_averageandreduce are in the process of being deprecated, and in the meantime,specifying either of those two args will overridereduction. 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()

Shape:

• Input:$(\ast )$(∗), where$\ast$∗ means any number of dimensions.

• Target:$(\ast )$(∗), same shape as the input.

• Output: scalar by default. Ifreduction is'none', then$(\ast )$(∗),the same shape as the input.

forward(log_input,target)[source]#

Runs the forward pass.

Return type

Tensor