Multilabel_soft_margin_loss — nnf_multilabel_soft_margin

Creates a criterion that optimizes a multi-label one-versus-all loss based on max-entropy, between input x and target y of size (N, C).

Usage

nnf_multilabel_soft_margin_loss(
  input,
  target,
  weight = NULL,
  reduction = "mean"
)

input: tensor (N,*) where ** means, any number of additional dimensions
target: tensor (N,*) , same shape as the input
weight: weight tensor to apply on the loss.
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'

It takes a one hot encoded target vector as input.