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Specifications and Common Mistakes

- Specifications and Common Mistakes:

- Misspellings or punctuation mistakes,incorrect formulas, abnormal display.

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mindspore.ops.cosine_embedding_loss

mindspore.ops.cosine_embedding_loss(input1, input2, target, margin=0.0, reduction='mean')

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

Given two tensors $input1$, $input2$, and a Tensor label $target$ with values 1 or -1:

$$\begin{array}{r}loss(input1,input2,target)=\{\begin{array}{ll}1-cos(input1,input2),& \text{if }target=1\\ max(0,cos(input1,input2)-margin),& \text{if }target=-1\end{array}\end{array}$$

Parameters

• input1 (Tensor) – Tensor of shape $(N,\ast )$ where $\ast$ means, any number of additional dimensions.

• input2 (Tensor) – Tensor of shape $(N,\ast )$, same shape and dtype as input1.

• target (Tensor) – Contains value 1 or -1. Suppose the shape of input1 is $(x_1,x_2,x_3,...,x_R)$, then the shape of target must be $(x_1,x_3,x_4,...,x_R)$.

• margin (float, optional) – 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.

Returns

Tensor or Scalar, if reduction is "none", its shape is the same as target. 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 [-1.0, 1.0].

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> intput1 = Tensor(np.array([[0.3, 0.8], [0.4, 0.3]]), mindspore.float32)
>>> intput2 = Tensor(np.array([[0.4, 1.2], [-0.4, -0.9]]), mindspore.float32)
>>> target = Tensor(np.array([1, -1]), mindspore.int32)
>>> output = ops.cosine_embedding_loss(intput1, intput2, target)
>>> print(output)
0.0003425479