`R/learning_rate_schedules.R`

`learning_rate_schedule_polynomial_decay.Rd`

A LearningRateSchedule that uses a polynomial decay schedule

```
learning_rate_schedule_polynomial_decay(
initial_learning_rate,
decay_steps,
end_learning_rate = 1e-04,
power = 1,
cycle = 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. Must be positive. See the decay computation above.- end_learning_rate
A scalar

`float32`

or`float64`

`Tensor`

or an R number. The minimal end learning rate.- power
A scalar

`float32`

or`float64`

`Tensor`

or an R number. The power of the polynomial. Defaults to linear, 1.0.- cycle
A boolean, whether or not it should cycle beyond decay_steps.

- ...
For backwards and forwards compatibility

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

It is commonly observed that a monotonically decreasing learning rate, whose
degree of change is carefully chosen, results in a better performing model.
This schedule applies a polynomial decay function to an optimizer step,
given a provided `initial_learning_rate`

, to reach an `end_learning_rate`

in the given `decay_steps`

.

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) {
step <- min(step, decay_steps)
((initial_learning_rate - end_learning_rate) *
(1 - step / decay_steps) ^ (power)
) + end_learning_rate
}
```

If `cycle`

is `TRUE`

then a multiple of `decay_steps`

is used, the first one
that is bigger than `step`

.

```
<- function(step) {
decayed_learning_rate <- decay_steps * ceiling(step / decay_steps)
decay_steps - end_learning_rate) *
((initial_learning_rate 1 - step / decay_steps) ^ (power)
(+ end_learning_rate
) }
```

You can pass this schedule directly into a keras Optimizer
as the `learning_rate`

.

Example: Fit a model while decaying from 0.1 to 0.01 in 10000 steps using sqrt (i.e. power=0.5):

```
...
starter_learning_rate <- 0.1
end_learning_rate <- 0.01
decay_steps <- 10000
learning_rate_fn <- learning_rate_schedule_polynomial_decay(
starter_learning_rate, decay_steps, end_learning_rate, power = 0.5)
model %>%
compile(optimizer = optimizer_sgd(learning_rate = learning_rate_fn),
loss = 'sparse_categorical_crossentropy',
metrics = 'accuracy')
model %>% fit(data, labels, epochs = 5)
```