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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.

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

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

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

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- Incorrect schematic or architecture diagrams.

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- Incorrect code.

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- Wrong screenshots.

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

Risk Warnings

- Risk Warnings:

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Content Compliance

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mindspore.nn.warmup_lr

mindspore.nn.warmup_lr(learning_rate, total_step, step_per_epoch, warmup_epoch)

Get learning rate warming up.

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

$$warmup\mathrm{\_}learning\mathrm{\_}rate[i]=learning\mathrm{\_}rate\ast tmp\mathrm{\_}epoch/tmp\mathrm{\_}warmup\mathrm{\_}epoch$$

Where $tmp\mathrm{\_}epoch=min(current\mathrm{\_}epoch,warmup\mathrm{\_}epoch),current\mathrm{\_}epoch=floor(\frac{i}{step\mathrm{\_}per\mathrm{\_}epoch})$

Parameters

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

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

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

• warmup_epoch (int) – A value that determines the epochs of the learning rate is warmed up.

Returns

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

Examples

>>> learning_rate = 0.1
>>> total_step = 6
>>> step_per_epoch = 2
>>> warmup_epoch = 2
>>> output = warmup_lr(learning_rate, total_step, step_per_epoch, warmup_epoch)
>>> print(output)
[0.0, 0.0, 0.05, 0.05, 0.1, 0.1]