Triplet margin with distance loss
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").
nn_triplet_margin_with_distance_loss( distance_function = NULL, margin = 1, swap = FALSE, reduction = "mean" )
distance_function |
(callable, optional): A nonnegative, real-valued function that
quantifies the closeness of two tensors. If not specified,
|
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: |
reduction |
(string, optional): Specifies the (optional) reduction to apply to the output:
|
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.
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.
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)
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