# A `LearningRateSchedule`

that uses an inverse time decay schedule.

Source: `R/optimizers-schedules.R`

`learning_rate_schedule_inverse_time_decay.Rd`

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

value to compute the decayed learning rate. You can
just pass a backend variable that you increment at each training step.

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 / (1 + decay_rate * step / decay_step)
}
```

or, if `staircase`

is `TRUE`

, as:

```
decayed_learning_rate <- function(step) {
initial_learning_rate /
(1 + decay_rate * floor(step / decay_step))
}
```

You can pass this schedule directly into a `optimizer_*`

as the learning rate.

## Usage

```
learning_rate_schedule_inverse_time_decay(
initial_learning_rate,
decay_steps,
decay_rate,
staircase = FALSE,
name = "InverseTimeDecay"
)
```

## Arguments

- initial_learning_rate
A float. The initial learning rate.

- decay_steps
How often to apply decay.

- decay_rate
A number. The decay rate.

- staircase
Whether to apply decay in a discrete staircase, as o pposed to continuous, fashion.

- name
String. Optional name of the operation. Defaults to

`"InverseTimeDecay"`

.

## 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

Fit a Keras model when decaying 1/t with a rate of 0.5:

```
...
initial_learning_rate <- 0.1
decay_steps <- 1.0
decay_rate <- 0.5
learning_rate_fn <- learning_rate_schedule_inverse_time_decay(
initial_learning_rate, decay_steps, decay_rate)
model %>% compile(
optimizer = optimizer_sgd(learning_rate=learning_rate_fn),
loss = 'sparse_categorical_crossentropy',
metrics = 'accuracy')
)
model %>% fit(data, labels, epochs=5)
```

## See also

Other optimizer learning rate schedules: `LearningRateSchedule()`

`learning_rate_schedule_cosine_decay()`

`learning_rate_schedule_cosine_decay_restarts()`

`learning_rate_schedule_exponential_decay()`

`learning_rate_schedule_piecewise_constant_decay()`

`learning_rate_schedule_polynomial_decay()`