mindspore.ops.triplet_margin_loss

mindspore.ops.triplet_margin_loss(anchor, positive, negative, margin=1.0, p=2, eps=1e-06, swap=False, reduction='mean')[source]

TripletMarginLoss operation. See mindspore.nn.TripletMarginLoss for details.

Parameters

  • anchor (Tensor) – A sample randomly selected from the training set. Data type must be BasicType.

  • positive (Tensor) – A sample belonging to the same category as anchor, with the same type and shape as anchor.

  • negative (Tensor) – A sample belonging to the different class from anchor, with the same type and shape as anchor.

  • margin (float, optional) – Make a margin between the positive pair and the negative pair. Default: 1.0.

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

  • eps (float, optional) – Add small value to avoid division by zero. Default: 1e-06.

  • swap (bool, optional) – The distance swap change the negative distance to the distance between positive sample and negative sample. Default: False.

  • reduction (str, optional) – Apply specific reduction method to the output: ‘none’, ‘mean’, ‘sum’. Default: ‘mean’.

Returns

Tensor. If reduction is “none”, its shape is \((N)\). Otherwise, a scalar value will be returned.

Raises

  • TypeError – If anchor or positive or ‘negative’ is not a Tensor.

  • TypeError – If dtype of anchor, positive and negative is not the same.

  • TypeError – If margin is not a float.

  • TypeError – If p is not an int.

  • TypeError – If eps is not a float.

  • TypeError – If swap is not a bool.

  • ValueError – If dimensions of input anchor, positive and negative are less than or equal to 1 at the same time.

  • ValueError – If the dimension of input anchor or positive or negative is bigger than or equal to 8.

  • ValueError – If shape of anchor, positive and negative cannot broadcast.

  • ValueError – If reduction is not one of ‘none’, ‘mean’, ‘sum’.

Supported Platforms:

GPU

Examples

>>> anchor = Tensor(np.array([[0.3, 0.7], [0.5, 0.5]]), mindspore.float32)
>>> positive = Tensor(np.array([[0.4, 0.6], [0.4, 0.6]]), mindspore.float32)
>>> negative = Tensor(np.array([[0.2, 0.9], [0.3, 0.7]]), mindspore.float32)
>>> output = ops.triplet_margin_loss(anchor, positive, negative)
>>> print(output)
0.8881968