nnf_cross_entropy: Cross_entropy

In torch: Tensors and Neural Networks with 'GPU' Acceleration

Cross_entropy

Description

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'

torch documentation built on May 29, 2024, 9:54 a.m.