mindspore.experimental.optim.lr_scheduler.CosineAnnealingWarmRestarts

class mindspore.experimental.optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, T_0, T_mult=1, eta_min=0, last_epoch=- 1)

Set the learning rate of each parameter group using a cosine annealing warm restarts schedule.

$${\eta }_t={\eta }_{min}+\frac{1}{2}({\eta }_{max}-{\eta }_{min})(1+\cos \left(\frac{T_{cur}}{T_i}\pi \right))$$

Where ${\eta }_{max}$ is set to the initial lr, ${\eta }_{min}$ is the minimum value for learning rate, ${\eta }_t$ is the current learning rate, $T_0$ is the number of iterations for the first restar, $T_i$ is the current number of iterations between two warm restarts in SGDR, $T_{cur}$ is the number of epochs since the last restart in SGDR.

When $T_{cur}=T_i$, set ${\eta }_t={\eta }_{min}$. When $T_{cur}=0$ after restart, set ${\eta }_t={\eta }_{max}$.

For more details, please refer to: SGDR: Stochastic Gradient Descent with Warm Restarts.

Warning

This is an experimental lr scheduler module that is subject to change. This module must be used with optimizers in Experimental Optimizer .

Parameters

• optimizer (mindspore.experimental.optim.Optimizer) – Wrapped optimizer.

• T_0 (int) – Number of iterations for the first restart.

• T_mult (int, optional) – A factor increases $T_i$ after a restart. Default: 1.

• eta_min (Union(float, int), optional) – Minimum learning rate. Default: 0.

• last_epoch (int, optional) – The number of times the step() method of the current learning rate adjustment strategy has been executed. Default: -1.

Raises

• ValueError – T_0 is less than or equal than 0 or not an int.

• ValueError – T_mult is less than or equal than 1 or not an int.

• ValueError – eta_min is not int or float.

Supported Platforms:

Ascend GPU CPU

Examples

>>> from mindspore.experimental import optim
>>> from mindspore import nn
>>> net = nn.Dense(3, 2)
>>> optimizer = optim.SGD(net.trainable_params(), lr=0.1, momentum=0.9)
>>> scheduler = optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, 2)
>>> iters = 3
>>> for epoch in range(2):
...     for i in range(iters):
...         scheduler.step(epoch + i / iters)
...         current_lr = scheduler.get_last_lr()
...         print(current_lr)
[Tensor(shape=[], dtype=Float32, value= 0.1)]
[Tensor(shape=[], dtype=Float32, value= 0.0933013)]
[Tensor(shape=[], dtype=Float32, value= 0.075)]
[Tensor(shape=[], dtype=Float32, value= 0.05)]
[Tensor(shape=[], dtype=Float32, value= 0.025)]
[Tensor(shape=[], dtype=Float32, value= 0.00669873)]

step(epoch=None)

Get the current learning rate and change the learning rate.

Parameters

epoch (int, optional) – The index of the last epoch. Default: None.