LinearLR

class torch.optim.lr_scheduler.LinearLR(optimizer, start_factor=0.3333333333333333, end_factor=1.0, total_iters=5, last_epoch=-1)

Decays the learning rate of each parameter group by linearly changing small multiplicative factor.

The multiplication is done until the number of epoch reaches a pre-defined milestone: total_iters. Notice that such decay can happen 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.

• start_factor (float) – The number we multiply learning rate in the first epoch. The multiplication factor changes towards end_factor in the following epochs. Default: 1./3.

• end_factor (float) – The number we multiply learning rate at the end of linear changing process. Default: 1.0.

• total_iters (int) – The number of iterations that multiplicative factor reaches to 1. 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 = 0.03125  if epoch == 1
>>> # lr = 0.0375   if epoch == 2
>>> # lr = 0.04375  if epoch == 3
>>> # lr = 0.05    if epoch >= 4
>>> scheduler = LinearLR(optimizer, start_factor=0.5, total_iters=4)
>>> for epoch in range(100):
>>>     train(...)
>>>     validate(...)
>>>     scheduler.step()

get_last_lr()

Return last computed learning rate by current scheduler.

Return type

list[float]

get_lr()

Compute the learning rate.

load_state_dict(state_dict)

Load the scheduler’s state.

Parameters

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

state_dict()

Return the state of the scheduler as a dict.

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

step(epoch=None)

Perform a step.