`R/learning_rate_schedules.R`

`learning_rate_schedule_inverse_time_decay.Rd`

A LearningRateSchedule that uses an inverse time decay schedule

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

- initial_learning_rate
A scalar

`float32`

or`float64`

`Tensor`

or an R number. The initial learning rate.- decay_steps
A scalar

`int32`

or`int64`

`Tensor`

or an R number. How often to apply decay.- decay_rate
An R number. The decay rate.

- staircase
Boolean. Whether to apply decay in a discrete staircase, as opposed to continuous, fashion.

- ...
For backwards and forwards compatibility

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

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 TensorFlow 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 keras Optimizer
as the `learning_rate`

.

Example: 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)
```