Source code for pypots.optim.lr_scheduler.exponential_lrs

Exponential learning rate scheduler.

# Created by Wenjie Du <>
# License: BSD-3-Clause

from .base import LRScheduler, logger

[docs] class ExponentialLR(LRScheduler): """Decays the learning rate of each parameter group by gamma every epoch. When last_epoch=-1, sets initial lr as lr. Parameters ---------- gamma: float, Multiplicative factor of learning rate decay. last_epoch: int The index of last epoch. Default: -1. verbose: bool If ``True``, prints a message to stdout for each update. Default: ``False``. Notes ----- This class works the same with ``torch.optim.lr_scheduler.ExponentialLR``. The only difference that is also why we implement them is that you don't have to pass according optimizers into them immediately while initializing them. Example ------- >>> scheduler = ExponentialLR(gamma=0.1) >>> adam = pypots.optim.Adam(lr=1e-3, lr_scheduler=scheduler) """ def __init__(self, gamma, last_epoch=-1, verbose=False): super().__init__(last_epoch, verbose) self.gamma = gamma
[docs] def get_lr(self): if not self._get_lr_called_within_step: logger.warning( "⚠️ To get the last learning rate computed by the scheduler, " "please use `get_last_lr()`.", ) if self.last_epoch == 0: return [group["lr"] for group in self.optimizer.param_groups] return [group["lr"] * self.gamma for group in self.optimizer.param_groups]
def _get_closed_form_lr(self): return [base_lr * self.gamma**self.last_epoch for base_lr in self.base_lrs]