Source code for netket.callbacks.convergence_stopping

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from dataclasses import dataclass

from collections import deque

import numpy as np


[docs] @dataclass class ConvergenceStopping: """A simple callback to stop the optimisation if the loss gets below a certain threshold. based on `driver._loss_name`.""" target: float """Target value for the monitored quantity. Training will stop if the driver hits the baseline.""" monitor: str = "mean" """Loss statistic to monitor. Should be one of 'mean', 'variance', 'sigma'.""" smoothing_window: int = 10 """ The loss is smoothed over the last `smoothing_window` iterations to reduce statistical fluctuations """ patience: int = 10 """ The loss must be consistently below this value for this number of iterations in order to stop the optimisation. """ def __post_init__(self): self._loss_window: deque = deque([], maxlen=self.smoothing_window) self._patience_counter: int = 0 def __call__(self, step, log_data, driver): """ A boolean function that determines whether or not to stop training. Args: step: An integer corresponding to the step (iteration or epoch) in training. log_data: A dictionary containing log data for training. driver: A NetKet variational driver. Returns: A boolean. If True, training continues, else, it does not. """ loss = np.real(getattr(log_data[driver._loss_name], self.monitor)) self._loss_window.append(loss) loss_smooth = np.mean(self._loss_window) if loss_smooth <= self.target: self._patience_counter += 1 else: self._patience_counter = 0 if self._patience_counter > self.patience: return False return True