Cross_entropy — nnf_cross

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")
)

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 $0 \leq targets [i] \leq C - 1$ , 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'