# nn_triplet_margin_with_distance_loss: Triplet margin with distance loss In torch: Tensors and Neural Networks with 'GPU' Acceleration

## Description

Creates a criterion that measures the triplet loss given input tensors a, p, and n (representing anchor, positive, and negative examples, respectively), and a nonnegative, real-valued function ("distance function") used to compute the relationship between the anchor and positive example ("positive distance") and the anchor and negative example ("negative distance").

## Usage

 1 2 3 4 5 6 nn_triplet_margin_with_distance_loss( distance_function = NULL, margin = 1, swap = FALSE, reduction = "mean" ) 

## Arguments

 distance_function (callable, optional): A nonnegative, real-valued function that quantifies the closeness of two tensors. If not specified, nn_pairwise_distance() will be used. Default: None margin (float, optional): A non-negative margin representing the minimum difference between the positive and negative distances required for the loss to be 0. Larger margins penalize cases where the negative examples are not distant enough from the anchors, relative to the positives. Default: 1. swap (bool, optional): Whether to use the distance swap described in the paper Learning shallow convolutional feature descriptors with triplet losses by V. Balntas, E. Riba et al. If TRUE, and if the positive example is closer to the negative example than the anchor is, swaps the positive example and the anchor in the loss computation. Default: FALSE. reduction (string, optional): Specifies the (optional) 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'

## Details

The unreduced loss (i.e., with reduction set to 'none') can be described as:

\ell(a, p, n) = L = \{l_1,…,l_N\}^\top, \quad l_i = \max \{d(a_i, p_i) - d(a_i, n_i) + {\rm margin}, 0\}

where N is the batch size; d is a nonnegative, real-valued function quantifying the closeness of two tensors, referred to as the distance_function; and margin is a non-negative margin representing the minimum difference between the positive and negative distances that is required for the loss to be 0. The input tensors have N elements each and can be of any shape that the distance function can handle. If reduction is not 'none' (default 'mean'), then:

\ell(x, y) = \begin{array}{ll} \mbox{mean}(L), & \mbox{if reduction} = \mbox{mean';}\\ \mbox{sum}(L), & \mbox{if reduction} = \mbox{sum'.} \end{array}

See also nn_triplet_margin_loss(), which computes the triplet loss for input tensors using the l_p distance as the distance function.

## Shape

• Input: (N, *) where * represents any number of additional dimensions as supported by the distance function.

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

## Examples

  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 if (torch_is_installed()) { # Initialize embeddings embedding <- nn_embedding(1000, 128) anchor_ids <- torch_randint(1, 1000, 1, dtype = torch_long()) positive_ids <- torch_randint(1, 1000, 1, dtype = torch_long()) negative_ids <- torch_randint(1, 1000, 1, dtype = torch_long()) anchor <- embedding(anchor_ids) positive <- embedding(positive_ids) negative <- embedding(negative_ids) # Built-in Distance Function triplet_loss <- nn_triplet_margin_with_distance_loss( distance_function=nn_pairwise_distance() ) output <- triplet_loss(anchor, positive, negative) # Custom Distance Function l_infinity <- function(x1, x2) { torch_max(torch_abs(x1 - x2), dim = 1)[[1]] } triplet_loss <- nn_triplet_margin_with_distance_loss( distance_function=l_infinity, margin=1.5 ) output <- triplet_loss(anchor, positive, negative) # Custom Distance Function (Lambda) triplet_loss <- nn_triplet_margin_with_distance_loss( distance_function = function(x, y) { 1 - nnf_cosine_similarity(x, y) } ) output <- triplet_loss(anchor, positive, negative) } 

torch documentation built on Oct. 7, 2021, 9:22 a.m.