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 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.
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 = 0.005 if epoch >= 4 >>> scheduler = LinearLR(self.opt, start_factor=0.5, total_iters=4) >>> for epoch in range(100): >>> train(...) >>> validate(...) >>> scheduler.step()
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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().
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print_lr(is_verbose, group, lr, epoch=None)¶ Display the current learning rate.
