nnf_cross_entropy: Cross_entropy

Description Usage Arguments

View source: R/nnf-loss.R

Description

This criterion combines log_softmax and nll_loss in a single function.

Usage

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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 ≥q 1 in the case of K-dimensional loss.

target

(Tensor) (N) where each value is 0 ≤q \mbox{targets}[i] ≤q C-1, or (N, d_1, d_2, ..., d_K) where K ≥q 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'


torch documentation built on Oct. 7, 2021, 9:22 a.m.