Source code for poutyne.framework.callbacks.earlystopping

"""
The source code of this file was copied from the Keras project, and has been modified. All modifications
made from the original source code are under the LGPLv3 license.

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"""

from typing import Dict

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 (str): 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 (str): 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: str = 'val_loss', min_delta: float = 0.0, patience: int = 0, verbose: bool = False, mode: str = 'min', ): super().__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(f"Invalid mode '{mode}'") self.mode = mode if mode == 'min': self.min_delta *= -1 self.monitor_op = np.less elif mode == 'max': self.min_delta *= 1 self.monitor_op = np.greater def on_train_begin(self, logs: Dict): # Allow instances to be re-used self.wait = 0 self.stopped_epoch = 0 self.best = np.Inf if self.mode == 'min' else -np.Inf def on_epoch_end(self, epoch_number: int, logs: Dict): 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_number self.model.stop_training = True if self.verbose: print(f'Epoch {self.stopped_epoch}: early stopping')