mindspore.experimental.optim.lr_scheduler.ExponentialLR

class mindspore.experimental.optim.lr_scheduler.ExponentialLR(optimizer, gamma, last_epoch=- 1)[source]

For each epoch, the learning rate decays exponentially, multiplied by gamma. 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.
gamma (float) – Learning rate scaling factor.
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
>>> class Net(nn.Cell):
...     def __init__(self):
...         super(Net, self).__init__()
...         self.fc = nn.Dense(16 * 5 * 5, 120)
...     def construct(self, x):
...         return self.fc(x)
>>> net = Net()
>>> optimizer = optim.Adam(net.trainable_params(), 0.01)
>>> scheduler = optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.5)
>>> for i in range(3):
...     scheduler.step()
...     current_lr = scheduler.get_last_lr()
...     print(current_lr)
[Tensor(shape=[], dtype=Float32, value= 0.005)]
[Tensor(shape=[], dtype=Float32, value= 0.0025)]
[Tensor(shape=[], dtype=Float32, value= 0.00125)]