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Specifications and Common Mistakes

- Specifications and Common Mistakes:

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mindspore.nn.CosineEmbeddingLoss

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

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:

$$\begin{array}{r}loss(x_1,x_2,y)=\{\begin{array}{ll}1-cos(x_1,x_2),& \text{if }y=1\\ max(0,cos(x_1,x_2)-margin),& \text{if }y=-1\end{array}\end{array}$$

Parameters

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

• 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.

Inputs:

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

• logits_x2 (Tensor) - Tensor of shape $(N,\ast )$, 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 CPU

Examples

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