A LearningRateSchedule that uses a piecewise constant decay schedule

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

## Arguments

boundaries

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

values

A list of Tensors 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'.

## Details

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.