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.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
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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