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'