A LearningRateSchedule that uses a piecewise constant decay schedule

```
learning_rate_schedule_piecewise_constant_decay(
boundaries,
values,
...,
name = NULL
)
```

- boundaries
A list of

`Tensor`

s or R numerics with strictly increasing entries, and with all elements having the same type as the optimizer step.- values
A list of

`Tensor`

s or R numerics that specifies the values for the intervals defined by`boundaries`

. It should have one more element than`boundaries`

, and all elements should have the same type.- ...
For backwards and forwards compatibility

- name
A string. Optional name of the operation. Defaults to 'PiecewiseConstant'.

The function returns a 1-arg callable to compute the piecewise constant when passed the current optimizer step. This can be useful for changing the learning rate value across different invocations of optimizer functions.

Example: use a learning rate that's 1.0 for the first 100001 steps, 0.5 for the next 10000 steps, and 0.1 for any additional steps.

```
step <- tf$Variable(0, trainable=FALSE)
boundaries <- as.integer(c(100000, 110000))
values <- c(1.0, 0.5, 0.1)
learning_rate_fn <- learning_rate_schedule_piecewise_constant_decay(
boundaries, values)
```# Later, whenever we perform an optimization step, we pass in the step.
learning_rate <- learning_rate_fn(step)

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

.