mlx.nn.losses.triplet_loss#
- class triplet_loss(anchors: array, positives: array, negatives: array, axis: int = -1, p: int = 2, margin: float = 1.0, eps: float = 1e-06, reduction: Literal['none', 'mean', 'sum'] = 'none')#
Computes the triplet loss for a set of anchor, positive, and negative samples. Margin is represented with alpha in the math section.
\[\max\left(\|A - P\|_p - \|A - N\|_p + \alpha, 0\right)\]- Parameters:
anchors (array) – The anchor samples.
positives (array) – The positive samples.
negatives (array) – The negative samples.
axis (int, optional) – The distribution axis. Default:
-1.p (int, optional) – The norm degree for pairwise distance. Default:
2.margin (float, optional) – Margin for the triplet loss. Defaults to
1.0.eps (float, optional) – Small positive constant to prevent numerical instability. Defaults to
1e-6.reduction (str, optional) – Specifies the reduction to apply to the output:
'none'|'mean'|'sum'. Default:'none'.
- Returns:
- Computed triplet loss. If reduction is “none”, returns a tensor of the same shape as input;
if reduction is “mean” or “sum”, returns a scalar tensor.
- Return type: