|TensorFlow 1 version||View source on GitHub|
Stop training when a monitored quantity has stopped improving.
Compat aliases for migration
See Migration guide for more details.
tf.keras.callbacks.EarlyStopping( monitor='val_loss', min_delta=0, patience=0, verbose=0, mode='auto', baseline=None, restore_best_weights=False )
||Quantity to be monitored.|
||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.|
||Number of epochs with no improvement after which training will be stopped.|
||Baseline value for the monitored quantity. Training will stop if the model doesn't show improvement over the baseline.|
||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.|
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))
get_monitor_value( logs )
set_model( model )
set_params( params )