EarlyStopping

[source]

EarlyStopping class

keras.callbacks.EarlyStopping(
    monitor="val_loss",
    min_delta=0,
    patience=0,
    verbose=0,
    mode="auto",
    baseline=None,
    restore_best_weights=False,
    start_from_epoch=0,
)

Stop training when a monitored metric has stopped improving.

Assuming the goal of a training is to minimize the loss. With this, the metric to be monitored would be 'loss', and mode would be 'min'. A model.fit() training loop will check at end of every epoch whether the loss is no longer decreasing, considering the min_delta and patience if applicable. Once it's found no longer decreasing, model.stop_training is marked True and the training terminates.

The quantity to be monitored needs to be available in logs dict. To make it so, pass the loss or metrics at model.compile().

Arguments

• monitor: Quantity to be monitored. Defaults to "val_loss".
• min_delta: 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. Defaults to 0.
• patience: Number of epochs with no improvement after which training will be stopped. Defaults to 0.
• verbose: Verbosity mode, 0 or 1. Mode 0 is silent, and mode 1 displays messages when the callback takes an action. Defaults to 0.
• mode: One of {"auto", "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; in "auto" mode, the direction is automatically inferred from the name of the monitored quantity. Defaults to "auto".
• baseline: Baseline value for the monitored quantity. If not None, training will stop if the model doesn't show improvement over the baseline. Defaults to None.
• restore_best_weights: Whether to restore model weights from the epoch with the best value of the monitored quantity. If False, the model weights obtained at the last step of training are used. An epoch will be restored regardless of the performance relative to the baseline. If no epoch improves on baseline, training will run for patience epochs and restore weights from the best epoch in that set. Defaults to False.
• start_from_epoch: Number of epochs to wait before starting to monitor improvement. This allows for a warm-up period in which no improvement is expected and thus training will not be stopped. Defaults to 0.

Example

>>> callback = keras.callbacks.EarlyStopping(monitor='loss',
...                                               patience=3)
>>> # This callback will stop the training when there is no improvement in
>>> # the loss for three consecutive epochs.
>>> model = keras.models.Sequential([keras.layers.Dense(10)])
>>> model.compile(keras.optimizers.SGD(), loss='mse')
>>> history = model.fit(np.arange(100).reshape(5, 20), np.zeros(5),
...                     epochs=10, batch_size=1, callbacks=[callback],
...                     verbose=0)
>>> len(history.history['loss'])  # Only 4 epochs are run.
4