Document feedback

Question document fragment

When a question document fragment contains a formula, it is displayed as a space.

Submission type

issue

It's a little complicated...

I'd like to ask someone.

Please select the submission type

Problem type

Specifications and Common Mistakes

- Specifications and Common Mistakes:

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

- Incorrect links, empty cells, or wrong formats.

- Chinese characters in English context.

- Minor inconsistencies between the UI and descriptions.

- Low writing fluency that does not affect understanding.

- Incorrect version numbers, including software package names and version numbers on the UI.

Usability

- Usability:

- Incorrect or missing key steps.

- Missing main function descriptions, keyword explanation, necessary prerequisites, or precautions.

- Ambiguous descriptions, unclear reference, or contradictory context.

- Unclear logic, such as missing classifications, items, and steps.

Correctness

- Correctness:

- Technical principles, function descriptions, supported platforms, parameter types, or exceptions inconsistent with that of software implementation.

- Incorrect schematic or architecture diagrams.

- Incorrect commands or command parameters.

- Incorrect code.

- Commands inconsistent with the functions.

- Wrong screenshots.

- Sample code running error, or running results inconsistent with the expectation.

Risk Warnings

- Risk Warnings:

- Lack of risk warnings for operations that may damage the system or important data.

Content Compliance

- Content Compliance:

- Contents that may violate applicable laws and regulations or geo-cultural context-sensitive words and expressions.

- Copyright infringement.

Please select the type of question

Problem description

Describe the bug so that we can quickly locate the problem.

Submit

Document feedback

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) – 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 $(N,\ast )$ 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

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