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.polynomial_decay_lr

mindspore.nn.polynomial_decay_lr(learning_rate, end_learning_rate, total_step, step_per_epoch, decay_epoch, power, update_decay_epoch=False)

Calculates learning rate base on polynomial decay function. The learning rate for each step will be stored in a list.

For the i-th step, the formula of computing decayed_learning_rate[i] is:

$$decayed\mathrm{\_}learning\mathrm{\_}rate[i]=(learning\mathrm{\_}rate-end\mathrm{\_}learning\mathrm{\_}rate)\ast (1-tmp\mathrm{\_}epoch/tmp\mathrm{\_}decay\mathrm{\_}epoch{)}^{power}+end\mathrm{\_}learning\mathrm{\_}rate$$

Where:

$$tmp\mathrm{\_}epoch=min(current\mathrm{\_}epoch,decay\mathrm{\_}epoch)$$

$$current\mathrm{\_}epoch=floor(\frac{i}{step\mathrm{\_}per\mathrm{\_}epoch})$$

$$tmp\mathrm{\_}decay\mathrm{\_}epoch=decay\mathrm{\_}epoch$$

If update_decay_epoch is true, update the value of tmp_decay_epoch every epoch. The formula is:

$$tmp\mathrm{\_}decay\mathrm{\_}epoch=decay\mathrm{\_}epoch\ast ceil(current\mathrm{\_}epoch/decay\mathrm{\_}epoch)$$

Parameters

• learning_rate (float) – The initial value of learning rate.

• end_learning_rate (float) – The end value of learning rate.

• total_step (int) – The total number of steps.

• step_per_epoch (int) – The number of steps in per epoch.

• decay_epoch (int) – Number of epochs to decay over.

• power (float) – The power of polynomial. It must be greater than 0.

• update_decay_epoch (bool) – If true, update decay_epoch. Default: False.

Returns

list[float]. The size of list is total_step.

Raises

• TypeError – If learning_rate or end_learning_rate or power is not a float.

• TypeError – If total_step or step_per_epoch or decay_epoch is not an int.

• TypeError – If update_decay_epoch is not a bool.

• ValueError – If learning_rate or power is not greater than 0.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore.nn as nn
>>>
>>> learning_rate = 0.1
>>> end_learning_rate = 0.01
>>> total_step = 6
>>> step_per_epoch = 2
>>> decay_epoch = 2
>>> power = 0.5
>>> r = nn.polynomial_decay_lr(learning_rate, end_learning_rate, total_step, step_per_epoch, decay_epoch, power)
>>> print(r)
[0.1, 0.1, 0.07363961030678928, 0.07363961030678928, 0.01, 0.01]