callback_reduce_lr_on_plateau {keras3}

Reduce learning rate when a metric has stopped improving.

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 = 10L,
  verbose = 0L,
  mode = "auto",
  min_delta = 1e-04,
  cooldown = 0L,
  min_lr = 0,
  ...
)

Arguments

monitor

String. Quantity to be monitored.

factor

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

patience

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

verbose

Integer. 0: quiet, 1: update messages.

mode

String. One of ⁠{'auto', 'min', 'max'}⁠. In 'min' mode, the learning rate 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

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

cooldown

Integer. Number of epochs to wait before resuming normal operation after the learning rate has been reduced.

min_lr

Float. Lower bound on the learning rate.

...

For forward/backward compatability.

Value

A Callback instance that can be passed to fit.keras.src.models.model.Model().

Examples

reduce_lr <- callback_reduce_lr_on_plateau(monitor = 'val_loss', factor = 0.2,
                                           patience = 5, min_lr = 0.001)
model %>% fit(x_train, y_train, callbacks = list(reduce_lr))

See Also

• https://keras.io/api/callbacks/reduce_lr_on_plateau#reducelronplateau-class

Other callbacks:
Callback()
callback_backup_and_restore()
callback_csv_logger()
callback_early_stopping()
callback_lambda()
callback_learning_rate_scheduler()
callback_model_checkpoint()
callback_remote_monitor()
callback_swap_ema_weights()
callback_tensorboard()
callback_terminate_on_nan()