PolynomialLR

class torch.optim.lr_scheduler.PolynomialLR(optimizer, total_iters=5, power=1.0, last_epoch=-1, verbose='deprecated')

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.

• verbose (bool | str) –

  If True, prints a message to stdout for each update. Default: False.

  Deprecated since version 2.2: verbose is deprecated. Please use get_last_lr() to access the learning rate.

Example

>>> # Assuming optimizer uses lr = 0.001 for all groups
>>> # lr = 0.001     if epoch == 0
>>> # lr = 0.00075   if epoch == 1
>>> # lr = 0.00050   if epoch == 2
>>> # lr = 0.00025   if epoch == 3
>>> # lr = 0.0       if epoch >= 4
>>> scheduler = PolynomialLR(optimizer, total_iters=4, power=1.0)
>>> for epoch in range(100):
>>>     train(...)
>>>     validate(...)
>>>     scheduler.step()

get_last_lr()

Return last computed learning rate by current scheduler.

Return type

List[float]

load_state_dict(state_dict)

Loads the schedulers state.

Parameters

state_dict (dict) – scheduler state. Should be an object returned from a call to state_dict().

print_lr(is_verbose, group, lr, epoch=None)

Display the current learning rate.

Deprecated since version 2.4: print_lr() is deprecated. Please use get_last_lr() to access the learning rate.

state_dict()

Returns the state of the scheduler as a dict.

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