callback_reduce_lr_on_plateau: Reduce learning rate when a metric has stopped improving.

Description Usage Arguments See Also

View source: R/callbacks.R

Description

Models often benefit from reducing the learning rate by a factor of 2-10 once learning stagnates. This callback monitors a quantity and if no improvement is seen for a 'patience' number of epochs, the learning rate is reduced.

Usage

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callback_reduce_lr_on_plateau(
  monitor = "val_loss",
  factor = 0.1,
  patience = 10,
  verbose = 0,
  mode = c("auto", "min", "max"),
  min_delta = 1e-04,
  cooldown = 0,
  min_lr = 0
)

Arguments

monitor

quantity to be monitored.

factor

factor by which the learning rate will be reduced. new_lr = lr

  • factor

patience

number of epochs with no improvement after which learning rate will be reduced.

verbose

int. 0: quiet, 1: update messages.

mode

one of "auto", "min", "max". In min mode, lr will be reduced when the quantity monitored has stopped decreasing; in max mode it will be reduced when the quantity monitored has stopped increasing; in auto mode, the direction is automatically inferred from the name of the monitored quantity.

min_delta

threshold for measuring the new optimum, to only focus on significant changes.

cooldown

number of epochs to wait before resuming normal operation after lr has been reduced.

min_lr

lower bound on the learning rate.

See Also

Other callbacks: callback_csv_logger(), callback_early_stopping(), callback_lambda(), callback_learning_rate_scheduler(), callback_model_checkpoint(), callback_progbar_logger(), callback_remote_monitor(), callback_tensorboard(), callback_terminate_on_naan()


keras documentation built on Feb. 10, 2022, 1:08 a.m.