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') [source]

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). By default, the losses are averaged over each loss element in the batch. Note that for some losses, there multiple elements per sample. If the field size_average is set to False, the losses are instead summed for each minibatch. Ignored when reduce is False. Default: True
• eps (float, optional) – Small value to avoid evaluation of $\log(0)$ when log_input=False. Default: 1e-8
• reduce (bool, optional) – Deprecated (see reduction). By default, the losses are averaged or summed over observations for each minibatch depending on size_average. When reduce is False, returns a loss per batch element instead and ignores size_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 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