Stop training when a monitored quantity has stopped improving.

Stop training when a monitored quantity has stopped improving.

callback_early_stopping(monitor = "val_loss", min_delta = 0,
  patience = 0, verbose = 0, mode = c("auto", "min", "max"),
  baseline = NULL, 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, 0 or 1.
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 to reach. 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.

See also

Other callbacks: callback_csv_logger, callback_lambda, callback_learning_rate_scheduler, callback_model_checkpoint, callback_progbar_logger, callback_reduce_lr_on_plateau, callback_remote_monitor, callback_tensorboard, callback_terminate_on_naan