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| import torch | |
| class EpsilonScheduler: | |
| def __init__( | |
| self, | |
| epsilon: float = 0.001, | |
| patience: int = 10, | |
| factor: float = 0.5, | |
| verbose: bool = False, | |
| ): | |
| self.epsilon = epsilon | |
| self.patience = patience | |
| self.factor = factor | |
| self.best = 1e16 | |
| self.count = 0 | |
| self.verbose = verbose | |
| def step(self, metric: float): | |
| if metric < self.best: | |
| self.best = metric | |
| self.count = 0 | |
| else: | |
| self.count += 1 | |
| if self.verbose: | |
| print(f"Train loss has not improved for {self.count} epochs.") | |
| if self.count >= self.patience: | |
| self.count = 0 | |
| self.epsilon *= self.factor | |
| if self.verbose: | |
| print(f"Reducing epsilon to {self.epsilon:0.2e}...") | |