learning_rate_schedule_exponential_decay: A 'LearningRateSchedule' that uses an exponential decay...
In keras3: R Interface to 'Keras'

learning_rate_schedule_exponential_decay

R Documentation

A `LearningRateSchedule` that uses an exponential decay schedule.

Description

When training a model, it is often useful to lower the learning rate as the training progresses. This schedule applies an exponential decay function to an optimizer step, given a provided initial learning rate.

The schedule is a 1-arg callable that produces a decayed learning rate when passed the current optimizer step. This can be useful for changing the learning rate value across different invocations of optimizer functions. It is computed as:

decayed_learning_rate <- function(step) {
  initial_learning_rate * decay_rate ^ (step / decay_steps)
}

If the argument staircase is TRUE, then step / decay_steps is an integer division and the decayed learning rate follows a staircase function.

You can pass this schedule directly into a optimizer as the learning rate.

Usage

learning_rate_schedule_exponential_decay(
  initial_learning_rate,
  decay_steps,
  decay_rate,
  staircase = FALSE,
  name = "ExponentialDecay"
)

Arguments

`initial_learning_rate`	A float. The initial learning rate.
`decay_steps`	A integer. Must be positive. See the decay computation above.
`decay_rate`	A float. The decay rate.
`staircase`	Boolean. If `TRUE` decay the learning rate at discrete intervals.
`name`	String. Optional name of the operation. Defaults to `⁠"ExponentialDecay⁠`".

Value

A 1-arg callable learning rate schedule that takes the current optimizer step and outputs the decayed learning rate, a scalar tensor of the same type as initial_learning_rate.

Examples

When fitting a Keras model, decay every 100000 steps with a base of 0.96:

initial_learning_rate <- 0.1
lr_schedule <- learning_rate_schedule_exponential_decay(
    initial_learning_rate,
    decay_steps=100000,
    decay_rate=0.96,
    staircase=TRUE)

model %>% compile(
  optimizer = optimizer_sgd(learning_rate = lr_schedule),
  loss = 'sparse_categorical_crossentropy',
  metrics = c('accuracy'))

model %>% fit(data, labels, epochs=5)

The learning rate schedule is also serializable and deserializable using keras$optimizers$schedules$serialize and keras$optimizers$schedules$deserialize.