keras: callback_early_stopping – R documentation

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keras

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

`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.

callback_early_stopping

Description

Usage

Arguments

See Also

keras

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