mindspore.nn.CosineEmbeddingLoss

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

Computes 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) - Input tensor.

  • logits_x2 (Tensor) - Its shape and data type must be the same as logits_x1’s shape and data type.

  • 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:

  • loss (Tensor) - If reduction is “none”, its shape is the same as labels’s shape, 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)
0.0003426075