mindspore.nn.warmup_lr

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

Gets learning rate warming up. The learning rate for each step will be stored in a list.

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

\[warmup\_learning\_rate[i] = learning\_rate * tmp\_epoch / warmup\_epoch\]

Where \(tmp\_epoch= \min(current\_epoch, warmup\_epoch),\ current\_epoch=floor(\frac{i}{step\_per\_epoch})\)

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.

Raises

• TypeError – If learning_rate is not a float.

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

• ValueError – If learning_rate is less than 0.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore.nn as nn
>>>
>>> learning_rate = 0.1
>>> total_step = 6
>>> step_per_epoch = 2
>>> warmup_epoch = 2
>>> lr = nn.warmup_lr(learning_rate, total_step, step_per_epoch, warmup_epoch)
>>> net = nn.Dense(2, 3)
>>> optim = nn.SGD(net.trainable_params(), learning_rate=lr)