ConstantLR#

classtorch.optim.lr_scheduler.ConstantLR(optimizer,factor=0.3333333333333333,total_iters=5,last_epoch=-1)[source]#

Multiply the learning rate of each parameter group by a small constant factor.

The multiplication is done until the number of epoch reaches a pre-defined milestone: total_iters.Notice that such multiplication of the small constant factor canhappen 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.

• factor (float) – The number we multiply learning rate until the milestone. Default: 1./3.

• total_iters (int) – The number of steps that the scheduler multiplies the learning rate by the factor.Default: 5.

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

Example

>>># Assuming optimizer uses lr = 0.05 for all groups>>># lr = 0.025   if epoch == 0>>># lr = 0.025   if epoch == 1>>># lr = 0.025   if epoch == 2>>># lr = 0.025   if epoch == 3>>># ...>>># lr = 0.05    if epoch >= 40>>>scheduler=ConstantLR(optimizer,factor=0.5,total_iters=40)>>>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.