mindspore.experimental.optim.lr_scheduler.ConstantLR

class mindspore.experimental.optim.lr_scheduler.ConstantLR(optimizer, factor=1.0 / 3, total_iters=5, last_epoch=- 1)[source]

Decays the learning rate of each parameter group by a small constant factor until the number of epoch reaches a pre-defined milestone: total_iters. Notice that such decay can happen simultaneously with other changes to the learning rate from outside this scheduler.

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.
factor (float, optional) – The factor number multiplied learning rate. Default: 1./3.
total_iters (int, optional) – The number of steps that the scheduler decays the learning rate, when the last_epoch reach total_iters, restore the learning rate. Default: 5.
last_epoch (int, optional) – The index of the last epoch. Default: -1.

Supported Platforms:: Ascend GPU CPU

Examples

>>> from mindspore import nn
>>> from mindspore.experimental import optim
>>> net = nn.Dense(2, 3)
>>> optimizer = optim.Adam(net.trainable_params(), 0.05)
>>> # Assuming optimizer uses lr = 0.05 for all groups
>>> # lr = 0.025   if epoch <4
>>> # lr = 0.05    if epoch >= 4
>>> scheduler = optim.lr_scheduler.ConstantLR(optimizer, factor=0.5, total_iters=4)
>>> for i in range(6):
...     scheduler.step()
...     current_lr = scheduler.get_last_lr()
...     print(current_lr)
[Tensor(shape=[], dtype=Float32, value= 0.025)]
[Tensor(shape=[], dtype=Float32, value= 0.025)]
[Tensor(shape=[], dtype=Float32, value= 0.025)]
[Tensor(shape=[], dtype=Float32, value= 0.05)]
[Tensor(shape=[], dtype=Float32, value= 0.05)]
[Tensor(shape=[], dtype=Float32, value= 0.05)]