MultiStepLR¶
-
class
torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones, gamma=0.1, last_epoch=- 1, verbose=False)[source]¶ Decays the learning rate of each parameter group by gamma once the number of epoch reaches one of the milestones. 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.
milestones (list) – List of epoch indices. Must be increasing.
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 < 80 >>> # lr = 0.0005 if epoch >= 80 >>> scheduler = MultiStepLR(optimizer, milestones=[30,80], 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.
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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.
