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
)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.
verbosity mode, 0 or 1.
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 value for the monitored quantity to reach. 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.
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()