ExponentialLR

class torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma, last_epoch=-1)

Decays the learning rate of each parameter group by gamma every epoch.

When last_epoch=-1, sets initial lr as lr.

Parameters

• optimizer (Optimizer) – Wrapped optimizer.

• gamma (float) – Multiplicative factor of learning rate decay.

• last_epoch (int) – The index of last epoch. Default: -1.

Example

>>> scheduler = ExponentialLR(optimizer, gamma=0.95)
>>> for epoch in range(100):
>>>     train(...)
>>>     validate(...)
>>>     scheduler.step()

get_last_lr()

Return last computed learning rate by current scheduler.

Return type

list[float]

get_lr()

Compute the learning rate of each parameter group.

Return type

list[float]

load_state_dict(state_dict)

Load the scheduler’s state.

Parameters

state_dict (dict) – scheduler state. Should be an object returned from a call to state_dict().

state_dict()

Return the state of the scheduler as a dict.

It contains an entry for every variable in self.__dict__ which is not the optimizer.

Return type

dict[str, Any]

step(epoch=None)

Perform a step.