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

In dfalbel/keras: R Interface to 'Keras'

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

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()

dfalbel/keras documentation built on Nov. 27, 2019, 8:16 p.m.