StepLR

class torch.optim.lr_scheduler.StepLR(optimizer, step_size, gamma=0.1, last_epoch=-1) [source]

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

Notice that such decay can happen simultaneously with other changes to the learning rate from outside this scheduler. When last_epoch=-1, sets initial lr as lr.

Parameters

• optimizer (Optimizer) – Wrapped optimizer.
• step_size (int) – Period of learning rate decay.
• gamma (float) – Multiplicative factor of learning rate decay. Default: 0.1.
• last_epoch (int) – The index of last epoch. Default: -1.

Example

>>> # Assuming optimizer uses lr = 0.05 for all groups
>>> # lr = 0.05     if epoch < 30
>>> # lr = 0.005    if 30 <= epoch < 60
>>> # lr = 0.0005   if 60 <= epoch < 90
>>> # ...
>>> scheduler = StepLR(optimizer, step_size=30, gamma=0.1)
>>> for epoch in range(100):
>>>     train(...)
>>>     validate(...)
>>>     scheduler.step()

get_last_lr() [source]

Return last computed learning rate by current scheduler.

Return type

list[float]

get_lr() [source]

Compute the learning rate of each parameter group.

Return type

list[float]

load_state_dict(state_dict) [source]

Load the scheduler’s state.

Parameters

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

state_dict() [source]

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) [source]

Perform a step.