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
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
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.
@struct.dataclass(_frozen=False)
class InvalidLossStopping:
"""A simple callback to stop NetKet if there are no more improvements in the training.
based on `driver._loss_name`."""
monitor: str = "mean"
"""Loss statistic to monitor. Should be one of 'mean', 'variance', 'sigma'."""
patience: Union[int, float] = 0
"""Number of epochs with invalid loss after which training will be stopped."""
_last_valid_iter: int = 0
"""Last valid iteration, to check against patience"""
[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