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
\((N, D)\).
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 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
reductionis'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()
}