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.

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()