class torch.optim.lr_scheduler.LinearLR(optimizer, start_factor=0.3333333333333333, end_factor=1.0, total_iters=5, last_epoch=-1, verbose='deprecated')[source]

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

  • 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.

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


>>> # 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()

Return last computed learning rate by current scheduler.


Loads the schedulers state.


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.


Returns the state of the scheduler as a dict.

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


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