This criterion combines log_softmax and nll_loss in a single function.

## Usage

nnf_cross_entropy(
input,
target,
weight = NULL,
ignore_index = -100,
reduction = c("mean", "sum", "none")
)

## Arguments

input

(Tensor) $$(N, C)$$ where C = number of classes or $$(N, C, H, W)$$ in case of 2D Loss, or $$(N, C, d_1, d_2, ..., d_K)$$ where $$K \geq 1$$ in the case of K-dimensional loss.

target

(Tensor) $$(N)$$ where each value is $$0 \leq \mbox{targets}[i] \leq C-1$$, or $$(N, d_1, d_2, ..., d_K)$$ where $$K \geq 1$$ for K-dimensional loss.

weight

(Tensor, optional) a manual rescaling weight given to each class. If given, has to be a Tensor of size C

ignore_index

(int, optional) Specifies a target value that is ignored and does not contribute to the input gradient.

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. Default: 'mean'