mindspore.nn.cosine_decay_lr
- mindspore.nn.cosine_decay_lr(min_lr, max_lr, total_step, step_per_epoch, decay_epoch)[source]
Calculates learning rate base on cosine 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:
\[decayed\_learning\_rate[i] = min\_lr + 0.5 * (max\_lr - min\_lr) * (1 + cos(\frac{current\_epoch}{decay\_epoch}\pi))\]Where \(current\_epoch=floor(\frac{i}{step\_per\_epoch})\).
- Parameters
- Returns
list[float]. The size of list is total_step.
- Raises
TypeError – If min_lr or max_lr is not a float.
TypeError – If total_step or step_per_epoch or decay_epoch is not an int.
ValueError – If max_lr is not greater than 0 or min_lr is less than 0.
ValueError – If total_step or step_per_epoch or decay_epoch is less than 0.
ValueError – If min_lr is greater than or equal to max_lr.
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> import mindspore.nn as nn >>> >>> min_lr = 0.01 >>> max_lr = 0.1 >>> total_step = 6 >>> step_per_epoch = 2 >>> decay_epoch = 2 >>> output = nn.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]