Triplet_margin_loss
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" )
anchor |
the anchor input tensor |
positive |
the positive input tensor |
negative |
the negative input tensor |
margin |
Default: 1. |
p |
The norm degree for pairwise distance. Default: 2. |
eps |
(float, optional) Small value to avoid division by zero. |
swap |
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: |
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. Default: 'mean' |
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.