mindspore.experimental.optim.lr_scheduler.CosineAnnealingLR

class mindspore.experimental.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max, eta_min=0.0, last_epoch=- 1)[source]

Set the learning rate of each parameter group using a cosine annealing lr schedule. 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, \(T_{max}\) is iteration number of cosine function, and \(T_{cur}\) is the number of epochs since the last restart in SGDR.

\[\begin{split}\begin{aligned} \eta_t & = \eta_{min} + \frac{1}{2}(\eta_{max} - \eta_{min})\left(1 + \cos\left(\frac{T_{cur}}{T_{max}}\pi\right)\right), & T_{cur} \neq (2k+1)T_{max}; \\ \eta_{t+1} & = \eta_{t} + \frac{1}{2}(\eta_{max} - \eta_{min}) \left(1 - \cos\left(\frac{1}{T_{max}}\pi\right)\right), & T_{cur} = (2k+1)T_{max}. \end{aligned}\end{split}\]

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_max (int) – Maximum number of iterations.
eta_min (float, optional) – Minimum learning rate. Default: 0.0.
last_epoch (int, optional) – The index of the last epoch. Default: -1.

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.CosineAnnealingLR(optimizer, T_max=2)
>>>
>>> for i in range(6):
...     scheduler.step()
...     current_lr = scheduler.get_last_lr()
...     print(current_lr)
[Tensor(shape=[], dtype=Float32, value= 0.05)]
[Tensor(shape=[], dtype=Float32, value= 0)]
[Tensor(shape=[], dtype=Float32, value= 0.05)]
[Tensor(shape=[], dtype=Float32, value= 0.1)]
[Tensor(shape=[], dtype=Float32, value= 0.05)]
[Tensor(shape=[], dtype=Float32, value= 0)]