keras3 (version 1.2.0)

callback_learning_rate_scheduler: Learning rate scheduler.

Description

At the beginning of every epoch, this callback gets the updated learning rate value from schedule function provided, with the current epoch and current learning rate, and applies the updated learning rate on the optimizer.

Usage

callback_learning_rate_scheduler(schedule, verbose = 0L)

Value

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

Arguments

schedule

A function that takes an epoch index (integer, indexed from 0) and current learning rate (float) as inputs and returns a new learning rate as output (float).

verbose

Integer. 0: quiet, 1: log update messages.

Examples

# This function keeps the initial learning rate steady for the first ten epochs
# and decreases it exponentially after that.
scheduler <- function(epoch, lr) {
  if (epoch < 10)
    return(lr)
  else
    return(lr * exp(-0.1))
}
model <- keras_model_sequential() |> layer_dense(units = 10)
model |> compile(optimizer = optimizer_sgd(), loss = 'mse')
model$optimizer$learning_rate |> as.array() |> round(5)

## [1] 0.01

callback <- callback_learning_rate_scheduler(schedule = scheduler)
history <- model |> fit(x = array(runif(100), c(5, 20)),
                        y = array(0, c(5, 1)),
                        epochs = 15, callbacks = list(callback), verbose = 0)
model$optimizer$learning_rate |> as.array() |> round(5)

## [1] 0.00607

See Also

• https://keras.io/api/callbacks/learning_rate_scheduler#learningratescheduler-class

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