nnf_multi_margin_loss: Multi_margin_loss

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

Multi_margin_loss

Description

Creates a criterion that optimizes a multi-class classification hinge loss (margin-based loss) between input x (a 2D mini-batch Tensor) and output y (which is a 1D tensor of target class indices, ⁠0 <= y <= x$size(2) - 1⁠ ).

Usage

nnf_multi_margin_loss(
  input,
  target,
  p = 1,
  margin = 1,
  weight = NULL,
  reduction = "mean"
)

Arguments

input	tensor (N,*) where ** means, any number of additional dimensions
target	tensor (N,*) , same shape as the input
p	Has a default value of 1. 1 and 2 are the only supported values.
margin	Has a default value of 1.
weight	a manual rescaling weight given to each class. If given, it has to be a Tensor of size C. Otherwise, it is treated as if having all ones.
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.