nnf_triplet_margin_loss: Triplet_margin_loss

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

Triplet_margin_loss

Description

Creates a criterion that measures the triplet loss given an input tensors x1 , x2 , x3 and a margin with a value greater than 0 . This is used for measuring a relative similarity between samples. A triplet is composed by a, p and n (i.e., anchor, positive examples and negative examples respectively). The shapes of all input tensors should be (N, D).

Usage

nnf_triplet_margin_loss(
  anchor,
  positive,
  negative,
  margin = 1,
  p = 2,
  eps = 1e-06,
  swap = FALSE,
  reduction = "mean"
)

Arguments

anchor	the anchor input tensor
positive	the positive input tensor
negative	the negative input tensor
margin	Default: 1.
p	The norm degree for pairwise distance. Default: 2.
eps	(float, optional) Small value to avoid division by zero.
swap	The distance swap is described in detail in the paper Learning shallow convolutional feature descriptors with triplet losses by V. Balntas, E. Riba et al. Default: FALSE.
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.