Table of Contents
Shortcuts

# TripletMarginLoss

class torch.nn.TripletMarginLoss(margin=1.0, p=2.0, eps=1e-06, swap=False, size_average=None, reduce=None, reduction='mean')

  Creates a criterion that measures the triplet loss given an input tensors x 1 x1 x 1, x 2 x2 x 2, x 3 x3 x 3 and a margin with a value greater than 0 0 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)(N, D)(N,D).

  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.

  The loss function for each sample in the mini-batch is:

  L(a,p,n)=max⁡{d(a i,p i)−d(a i,n i)+m a r g i n,0}L(a, p, n) = \max \{d(a_i, p_i) - d(a_i, n_i) + {\rm margin}, 0\} L(a,p,n)=max{d(a i​,p i​)−d(a i​,n i​)+margin,0}

  where

  d(x i,y i)=∥x i−y i∥p d(x_i, y_i) = \left\lVert {\bf x}_i - {\bf y}_i \right\rVert_p d(x i​,y i​)=∥x i​−y i​∥p​

  The norm is calculated using the specified p value and a small constant ε\varepsilon ε is added for numerical stability.

  See also `TripletMarginWithDistanceLoss`, which computes the triplet margin loss for input tensors using a custom distance function.

  Parameters:

    - margin (float, optional) – Default: 1 1 1.

    - p (int, optional) – The norm degree for pairwise distance. Default: 2 2 2.

    - eps (float, optional) – Small constant for numerical stability. Default: 1 e−6 1e-6 1 e−6.

    - swap (bool, optional) – 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`.

    - size_average (bool, optional) – Deprecated (see `reduction`). By default, the losses are averaged over each loss element in the batch. Note that for some losses, there are multiple elements per sample. If the field `size_average` is set to `False`, the losses are instead summed for each minibatch. Ignored when `reduce` is `False`. Default: `True`

    - reduce (bool, optional) – Deprecated (see `reduction`). By default, the losses are averaged or summed over observations for each minibatch depending on `size_average`. When `reduce` is `False`, returns a loss per batch element instead and ignores `size_average`. Default: `True`

    - reduction (str, 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. Note: `size_average` and `reduce` are in the process of being deprecated, and in the meantime, specifying either of those two args will override `reduction`. Default: `'mean'`

  Shape:

    - Input: (N,D)(N, D)(N,D) or (D)(D)(D) where D D D is the vector dimension.

    - Output: A Tensor of shape (N)(N)(N) if `reduction` is `'none'` and input shape is (N,D)(N, D)(N,D); a scalar otherwise.

  Examples:

      >>> triplet_loss = nn.TripletMarginLoss(margin=1.0, p=2, eps=1e-7)
      >>> anchor = torch.randn(100, 128, requires_grad=True)
      >>> positive = torch.randn(100, 128, requires_grad=True)
      >>> negative = torch.randn(100, 128, requires_grad=True)
      >>> output = triplet_loss(anchor, positive, negative)
      >>> output.backward()

## Docs

Access comprehensive developer documentation for PyTorch

View Docs

## Tutorials

Get in-depth tutorials for beginners and advanced developers

View Tutorials

## Resources

Find development resources and get your questions answered

View Resources

To analyze traffic and optimize your experience, we serve cookies on this site. By clicking or navigating, you agree to allow our usage of cookies. As the current maintainers of this site, Facebook’s Cookies Policy applies. Learn more, including about available controls: Cookies Policy.