Reduce learning rate when a metric has stopped improving.

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.

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