callback_early_stopping
Stop training when a monitored quantity has stopped improving.
Description
Stop training when a monitored quantity has stopped improving.
Usage
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
| Arguments | Description |
|---|---|
| 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()