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)

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.

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