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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

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- 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.

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mindelec.common.LearningRate

class mindelec.common.LearningRate(learning_rate, end_learning_rate, warmup_steps, decay_steps, power)

Warmup learning rate and decay learning rate. Return warmup learning rate when warmup_steps is greater than 0. Otherwise, return decay learning rate.

Parameters

• learning_rate (float) – positive float type number of basic learning rate.

• end_learning_rate (float) – non-negtive float type number of end learning rate.

• warmup_steps (int) – non-negtive int type number of warmup steps.

• decay_steps (int) – A positive int value used to calculate decayed learning rate.

• power (float) – A positive float value used to calculate decayed learning rate.

Inputs:

• global_step (Tensor) - The current step number with shape $()$.

Returns

Tensor. The learning rate value for the current step with shape $()$.

Supported Platforms:

Ascend

Examples

>>> from mindelec.common import LearningRate
>>> from mindspore.common.tensor import Tensor
>>> from mindspore.common import dtype as mstype
>>> lr = LearningRate(0.1, 0.001, 0, 10, 0.5)
>>> print(lr(Tensor(1000, mstype.int32)))
0.001