nnf_triplet_margin_loss function

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"
)

Arguments

• 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: 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. Default: 'mean'