# nn_poisson_nll_loss: Poisson NLL loss In torch: Tensors and Neural Networks with 'GPU' Acceleration

## Description

Negative log likelihood loss with Poisson distribution of target. The loss can be described as:

## Usage

 1 2 3 4 5 6 nn_poisson_nll_loss( log_input = TRUE, full = FALSE, eps = 1e-08, reduction = "mean" ) 

## Arguments

 log_input (bool, optional): if TRUE the loss is computed as \exp(\mbox{input}) - \mbox{target}*\mbox{input}, if FALSE the loss is \mbox{input} - \mbox{target}*\log(\mbox{input}+\mbox{eps}). full (bool, optional): whether to compute full loss, i. e. to add the Stirling approximation term \mbox{target}*\log(\mbox{target}) - \mbox{target} + 0.5 * \log(2π\mbox{target}). eps (float, optional): Small value to avoid evaluation of \log(0) when log_input = FALSE. Default: 1e-8 reduction (string, 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 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'

## Details

\mbox{target} \sim \mathrm{Poisson}(\mbox{input}) \mbox{loss}(\mbox{input}, \mbox{target}) = \mbox{input} - \mbox{target} * \log(\mbox{input}) + \log(\mbox{target!})

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.

## Shape

• Input: (N, *) where * means, any number of additional dimensions

• Target: (N, *), same shape as the input

• Output: scalar by default. If reduction is 'none', then (N, *), the same shape as the input

## Examples

 1 2 3 4 5 6 7 8 if (torch_is_installed()) { loss <- nn_poisson_nll_loss() log_input <- torch_randn(5, 2, requires_grad=TRUE) target <- torch_randn(5, 2) output <- loss(log_input, target) output\$backward() } 

### Example output




torch documentation built on Oct. 7, 2021, 9:22 a.m.