mindspore.nn.cosine_decay_lr

mindspore.nn.cosine_decay_lr(min_lr, max_lr, total_step, step_per_epoch, decay_epoch)

Calculate learning rate base on cosine decay function.

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

$$decayed\mathrm{\_}learning\mathrm{\_}rate[i]=min\mathrm{\_}learning\mathrm{\_}rate+0.5\ast (max\mathrm{\_}learning\mathrm{\_}rate-min\mathrm{\_}learning\mathrm{\_}rate)\ast (1+cos(\frac{current\mathrm{\_}epoch}{decay\mathrm{\_}epoch}\pi ))$$

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

Parameters

• min_lr (float) – The minimum value of learning rate.

• max_lr (float) – The maximum 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) – A value used to calculate decayed learning rate.

Returns

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

Examples

>>> min_lr = 0.01
>>> max_lr = 0.1
>>> total_step = 6
>>> step_per_epoch = 2
>>> decay_epoch = 2
>>> output = cosine_decay_lr(min_lr, max_lr, total_step, step_per_epoch, decay_epoch)
>>> print(output)
[0.1, 0.1, 0.05500000000000001, 0.05500000000000001, 0.01, 0.01]