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"))

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.

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