A LearningRateSchedule that uses an exponential decay schedule

learning_rate_schedule_exponential_decay(
initial_learning_rate,
decay_steps,
decay_rate,
staircase = FALSE,
...,
name = NULL
)

## Arguments

initial_learning_rate

A scalar float32 or float64 Tensor or a R number. The initial learning rate.

decay_steps

A scalar int32 or int64 Tensor or an R number. Must be positive. See the decay computation above.

decay_rate

A scalar float32 or float64 Tensor or an R number. The decay rate.

staircase

Boolean. If TRUE decay the learning rate at discrete intervals.

...

For backwards and forwards compatibility

name

String. Optional name of the operation. Defaults to 'ExponentialDecay'.

## Details

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 (see example) Example: 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 = 'accuracy')

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