mindspore.nn.CosineEmbeddingLoss

class mindspore.nn.CosineEmbeddingLoss(margin=0.0, reduction='mean')[source]

CosineEmbeddingLoss creates a criterion to measure the similarity between two tensors using cosine distance.

Given two tensors \(x1\), \(x2\), and a Tensor label \(y\) with values 1 or -1:

\[\begin{split}loss(x_1, x_2, y) = \begin{cases} 1-cos(x_1, x_2), & \text{if } y = 1\\ max(0, cos(x_1, x_2)-margin), & \text{if } y = -1\\ \end{cases}\end{split}\]
Parameters

  • margin (float) – Should be in [-1.0, 1.0]. Default 0.0.

  • reduction (str) – Specifies which reduction to be applied to the output. It must be one of “none”, “mean”, and “sum”, meaning no reduction, reduce mean and sum on output, respectively. Default “mean”.

Inputs:

  • logits_x1 (Tensor) - Tensor of shape \((N, *)\) where \(*\) means, any number of additional dimensions.

  • logits_x2 (Tensor) - Tensor of shape \((N, *)\), same shape and dtype as logits_x1.

  • labels (Tensor) - Contains value 1 or -1. Suppose the shape of logits_x1 is \((x_1, x_2, x_3, ..., x_R)\), then the shape of labels must be \((x_1, x_3, x_4, ..., x_R)\).

Outputs:

Tensor or Scalar, if reduction is “none”, its shape is the same as labels. Otherwise, a scalar value will be returned.

Raises

  • TypeError – If margin is not a float.

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

  • ValueError – If margin is not in range [-1, 1].

Supported Platforms:

Ascend GPU

Examples

>>> logits_x1 = Tensor(np.array([[0.3, 0.8], [0.4, 0.3]]), mindspore.float32)
>>> logits_x2 = Tensor(np.array([[0.4, 1.2], [-0.4, -0.9]]), mindspore.float32)
>>> labels = Tensor(np.array([1, -1]), mindspore.int32)
>>> cosine_embedding_loss = nn.CosineEmbeddingLoss()
>>> output = cosine_embedding_loss(logits_x1, logits_x2, labels)
>>> print(output)
0.0003425479