PolynomialLR

class torch.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)
>>> 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.

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.