MultiStepLR#

classtorch.optim.lr_scheduler.MultiStepLR(optimizer,milestones,gamma=0.1,last_epoch=-1)[source]#

Decays the learning rate of each parameter group by gamma once the number of epoch reaches one of the milestones.

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.

• milestones (list) – List of epoch indices. Must be increasing.

• 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 < 80>>># lr = 0.0005   if epoch >= 80>>>scheduler=MultiStepLR(optimizer,milestones=[30,80],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.