Source code for pytoune.framework.callbacks.earlystopping

"""
The source code of this file was copied from the Keras project, and has been
modified.

COPYRIGHT

All contributions by François Chollet:
Copyright (c) 2015, François Chollet.
All rights reserved.

All contributions by Google:
Copyright (c) 2015, Google, Inc.
All rights reserved.

All contributions by Microsoft:
Copyright (c) 2017, Microsoft, Inc.
All rights reserved.

All other contributions:
Copyright (c) 2015 - 2017, the respective contributors.
All rights reserved.

Each contributor holds copyright over their respective contributions.
The project versioning (Git) records all such contribution source information.

LICENSE

The MIT License (MIT)

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
"""

import numpy as np
from .callbacks import Callback

[docs]class EarlyStopping(Callback): """ The source code of this class is under the MIT License and was copied from the Keras project, and has been modified. Stop training when a monitored quantity has stopped improving. Args: monitor (int): Quantity to be monitored. min_delta (float): Minimum change in the monitored quantity to qualify as an improvement, i.e. an absolute change of less than min_delta, will count as no improvement. (Default value = 0) patience (int): Number of epochs with no improvement after which training will be stopped. (Default value = 0) verbose (bool): Whether to print when early stopping is done. (Default value = False) mode (string): One of {min, max}. In `min` mode, training will stop when the quantity monitored has stopped decreasing; in `max` mode it will stop when the quantity monitored has stopped increasing. (Default value = 'min') """ def __init__(self, *, monitor='val_loss', min_delta=0, patience=0, verbose=False, mode='min'): super(EarlyStopping, self).__init__() self.monitor = monitor self.patience = patience self.verbose = verbose self.min_delta = min_delta self.wait = 0 self.stopped_epoch = 0 if mode not in ['min', 'max']: raise ValueError("Invalid mode '%s'" % mode) if mode == 'min': self.monitor_op = np.less elif mode == 'max': self.monitor_op = np.greater if self.monitor_op == np.greater: self.min_delta *= 1 else: self.min_delta *= -1 def on_train_begin(self, logs): # Allow instances to be re-used self.wait = 0 self.stopped_epoch = 0 self.best = np.Inf if self.monitor_op == np.less else -np.Inf def on_epoch_end(self, epoch, logs): current = logs[self.monitor] if self.monitor_op(current - self.min_delta, self.best): self.best = current self.wait = 0 else: self.wait += 1 if self.wait >= self.patience: self.stopped_epoch = epoch self.model.stop_training = True def on_train_end(self, logs): if self.stopped_epoch > 0 and self.verbose: print('Epoch %05d: early stopping' % (self.stopped_epoch + 1))