Source code for netket.callbacks.invalidlossstopping

# Copyright 2020, 2021 The NetKet Authors - All rights reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# Unless required by applicable law or agreed to in writing, software
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from typing import Union
import numpy as np

from netket.utils import struct

# Mark this class a NetKet dataclass so that it can automatically be serialized by Flax.
[docs] class InvalidLossStopping(struct.Pytree, mutable=True): """A simple callback to stop NetKet if there are no more improvements in the training. based on `driver._loss_name`.""" monitor: str """Loss statistic to monitor. Should be one of 'mean', 'variance', 'sigma'.""" patience: Union[int, float] """Number of epochs with invalid loss after which training will be stopped.""" # caches _last_valid_iter: int """Last valid iteration, to check against patience"""
[docs] def __init__(self, monitor: str = "mean", patience: Union[int, float] = 0): """ Construct a callback stopping theoptimisation when the monitored quantity becaomes invalid for at least `patience` steps. Args: monitor: a string with the name of the quantity to be monitored. This is applied to the standard loss optimised by a driver, such as the Energy for the VMC driver (default: 'mean'). patience: Number of steps to wait before stopping the execution after the tracked quantity becomes invalid (default 0, meaning that it stops immediately). """ self.monitor = monitor self.patience = patience # caches self._last_valid_iter = 0
[docs] 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)) if not np.isfinite(loss): if step - self._last_valid_iter >= self.patience: return False else: self._last_valid_iter = step return True