StepLR#

classtorch.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 ratefrom 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)>>>forepochinrange(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 returnedfrom a call tostate_dict().

state_dict()[source]#

Return the state of the scheduler as adict.

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

Return type

dict[str,Any]

step(epoch=None)[source]#

Perform a step.