A LearningRateSchedule that uses an exponential decay schedule

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

- 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'.

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)