PolynomialLR#

classtorch.optim.lr_scheduler.PolynomialLR(optimizer,total_iters=5,power=1.0,last_epoch=-1)[source]#

Decays the learning rate of each parameter group using a polynomial function in the given total_iters.

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

Parameters

• optimizer (Optimizer) – Wrapped optimizer.

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

• power (float) – The power of the polynomial. Default: 1.0.

Example

>>># Assuming optimizer uses lr = 0.05 for all groups>>># lr = 0.0490   if epoch == 0>>># lr = 0.0481   if epoch == 1>>># lr = 0.0472   if epoch == 2>>># ...>>># lr = 0.0      if epoch >= 50>>>scheduler=PolynomialLR(optimizer,total_iters=50,power=0.9)>>>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.

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.