StepLR¶
-
class
torch.optim.lr_scheduler.StepLR(optimizer, step_size, gamma=0.1, last_epoch=- 1, verbose=False)[source]¶ Decays the learning rate of each parameter group by gamma every step_size epochs. 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.
step_size (int) – Period of learning rate decay.
gamma (float) – Multiplicative factor of learning rate decay. Default: 0.1.
last_epoch (int) – The index of 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 = 0.05 if epoch < 30 >>> # lr = 0.005 if 30 <= epoch < 60 >>> # lr = 0.0005 if 60 <= epoch < 90 >>> # ... >>> scheduler = StepLR(optimizer, step_size=30, gamma=0.1) >>> 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.
