nn_poisson_nll_loss function

Poisson NLL loss

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

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 $\mbox{target}*\log(\mbox{target}) - \mbox{target} + 0.5 * \log(2\pi\mbox{target})$.
• 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.

Details

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

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()
}