tf.keras.callbacks.EarlyStopping

Stop training when a monitored quantity has stopped improving.

Inherits From: Callback

View aliases

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.keras.callbacks.EarlyStopping

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

Arguments
monitor	Quantity to be monitored.
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.
patience	Number of epochs with no improvement after which training will be stopped.
verbose	verbosity mode.
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.
baseline	Baseline value for the monitored quantity. Training will stop if the model doesn't show improvement over the baseline.
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.

Example:

callback = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=3)
# This callback will stop the training when there is no improvement in
# the validation loss for three consecutive epochs.
model.fit(data, labels, epochs=100, callbacks=[callback],
    validation_data=(val_data, val_labels))

Methods

get_monitor_value

View source

get_monitor_value(
    logs
)

set_model

View source

set_model(
    model
)

set_params

View source

set_params(
    params
)