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).
nnf_triplet_margin_loss(
anchor,
positive,
negative,
margin = 1,
p = 2,
eps = 1e-06,
swap = FALSE,
reduction = "mean"
)the anchor input tensor
the positive input tensor
the negative input tensor
Default: 1.
The norm degree for pairwise distance. Default: 2.
(float, optional) Small value to avoid division by zero.
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.
(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'