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" )
log_input |
(bool, optional): if |
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
|
reduction |
(string, optional): Specifies the reduction to apply to the output:
|
\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.
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
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()
}Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.