On this page
LinearLR
class torch.optim.lr_scheduler.LinearLR(optimizer, start_factor=0.3333333333333333, end_factor=1.0, total_iters=5, last_epoch=1, verbose=False)
[source]
Decays the learning rate of each parameter group by linearly changing small multiplicative factor until the number of epoch reaches a predefined 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.
 verbose (bool) – If
True
, prints a message to stdout for each update. Default:False
.
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(self.opt, 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.
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.
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.
© 2024, PyTorch Contributors
PyTorch has a BSDstyle license, as found in the LICENSE file.
https://pytorch.org/docs/2.1/generated/torch.optim.lr_scheduler.LinearLR.html