torch.nn.functional.poisson_nll_loss

torch.nn.functional.poisson_nll_loss(input, target, log_input=True, full=False, size_average=None, eps=1e-08, reduce=None, reduction='mean')

Compute the Poisson negative log likelihood loss.

See PoissonNLLLoss for details.

Parameters

• input (Tensor) – Expectation of underlying Poisson distribution.

• target (Tensor) – Random sample $target \sim \text{Poisson}(input)$.

• log_input (bool) – If True the loss is computed as $\exp(\text{input}) - \text{target} * \text{input}$, if False then loss is $\text{input} - \text{target} * \log(\text{input}+\text{eps})$. Default: True

• full (bool) – Whether to compute full loss, i. e. to add the Stirling approximation term. Default: False $\text{target} * \log(\text{target}) - \text{target} + 0.5 * \log(2 * \pi * \text{target})$.

• size_average (bool, optional) – Deprecated (see reduction).

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

• reduce (bool, optional) – Deprecated (see reduction).

• 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 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'

Return type

Tensor