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.

PR

Just a small problem.

I can fix it online!

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

mindflow.common.get_poly_lr

mindflow.common.get_poly_lr(global_step, lr_init, lr_end, lr_max, warmup_steps, total_steps, poly_power)

Generate polynomial decay learning rate array. The learning rate decays in a polynomial manner as training goes along. it follows $lr=step\ast (lr\mathrm{\_}max-lr\mathrm{\_}init)/warmup\mathrm{\_}steps$ , then $lr=lr\mathrm{\_}end+(lr\mathrm{\_}max-lr\mathrm{\_}end)\ast [(1-i+step)/(total\mathrm{\_}steps-warmup\mathrm{\_}steps)]\ast \ast poly\mathrm{\_}power$

Parameters

• global_step (int) – current step number, non-negtive int value.

• lr_init (float) – init learning rate, positive float value.

• lr_end (float) – end learning rate, non-negtive float value.

• lr_max (float) – max learning rate, positive float value.

• warmup_steps (int) – number of warmup epochs, non-negtive int value.

• total_steps (int) – total epoch of training, positive int value.

• poly_power (float) – poly learning rate power, positive float value.

Returns

Numpy.array, learning rate array.

Supported Platforms:

Ascend GPU

Examples

>>> from mindflow.common import get_poly_lr
>>> learning_rate = get_poly_lr(100, 0.001, 0.1, 0.0001, 1000, 10000, 0.5)
>>> print(learning_rate.shape)
(9900,)