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).

## Usage

```
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'