ConstantLR¶
-
class
torch.optim.lr_scheduler.ConstantLR(optimizer, factor=0.3333333333333333, total_iters=5, last_epoch=- 1, verbose=False)[source]¶ Decays the learning rate of each parameter group by a small constant 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.
factor (float) – The number we multiply learning rate until the milestone. Default: 1./3.
total_iters (int) – The number of steps that the scheduler decays the learning rate. 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 = 0.025 if epoch == 1 >>> # lr = 0.025 if epoch == 2 >>> # lr = 0.025 if epoch == 3 >>> # lr = 0.05 if epoch >= 4 >>> scheduler = ConstantLR(self.opt, 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.
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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.
