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)]