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'
.'none'
: no reduction will be applied.'mean'
: compute and return the mean of elements in the output.'sum'
: the output elements will be summed.
- 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
>>> import mindspore >>> import numpy as np >>> from mindspore import Tensor, ops >>> 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