nn_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

nn_triplet_margin_loss(
  margin = 1,
  p = 2,
  eps = 1e-06,
  swap = FALSE,
  reduction = "mean"
)

Arguments

margin	(float, optional): Default: 1.
p	(int, optional): The norm degree for pairwise distance. Default: 2.
eps	constant to avoid NaN's
swap	(bool, optional): 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.

Details

The distance swap is described in detail in the paper Learning shallow convolutional feature descriptors with triplet losses \Sexpr[results=rd]{tools:::Rd_expr_doi("10.5244/C.30.119")} by V. Balntas, E. Riba et al.

The loss function for each sample in the mini-batch is:

L(a, p, n) = \max \{d(a_i, p_i) - d(a_i, n_i) + {\rm margin}, 0\}

where

d(x_i, y_i) = | {\bf x}_i - {\bf y}_i |_p

See also nn_triplet_margin_with_distance_loss(), which computes the triplet margin loss for input tensors using a custom distance function.

Shape

• Input: (N, D) where D is the vector dimension.

• Output: A Tensor of shape (N) if reduction is 'none', or a scalar otherwise.

Examples

if (torch_is_installed()) {
triplet_loss <- nn_triplet_margin_loss(margin = 1, p = 2)
anchor <- torch_randn(100, 128, requires_grad = TRUE)
positive <- torch_randn(100, 128, requires_grad = TRUE)
negative <- torch_randn(100, 128, requires_grad = TRUE)
output <- triplet_loss(anchor, positive, negative)
output$backward()
}

torch documentation built on Aug. 21, 2025, 5:50 p.m.