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.train.Loss

class mindspore.train.Loss

Calculates the average of the loss. If method ‘update’ is called every $n$ iterations, the result of evaluation will be:

$$loss=\frac{\sum _{k=1}^{n}los{s}_k}{n}$$

Supported Platforms:

Ascend GPU CPU

Examples

>>> import numpy as np
>>> import mindspore
>>> from mindspore import Tensor
>>> from mindspore.train import Loss
>>>
>>> x = Tensor(np.array(0.2), mindspore.float32)
>>> loss = Loss()
>>> loss.clear()
>>> loss.update(x)
>>> result = loss.eval()
>>> print(result)
0.20000000298023224

clear()

Clears the internal evaluation result.

eval()

Calculates the average of the loss.

Returns

numpy.float64. The average of the loss.

Raises

RuntimeError – If the total number is 0.

update(*inputs)

Updates the internal evaluation result.

Parameters

inputs – Inputs contain only one element, the element is loss. The dimension of loss must be 0 or 1.

Raises

• ValueError – If the length of inputs is not 1.

• ValueError – If the dimension of loss is not 1 or 0.