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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.


  name = "PiecewiseConstant"



A list of Python numbers with strictly increasing entries, and with all elements having the same type as the optimizer step.


A list of Python numbers 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.


A string. Optional name of the operation. Defaults to "PiecewiseConstant".


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 the boundary tensors.

The output of the 1-arg function that takes the step is values[0] when step <= boundaries[0], values[1] when step > boundaries[0] and step <= boundaries[1], ..., and values[-1] when step > boundaries[-1].


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 <- 0
boundaries <- 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 optimizer as the learning rate. The learning rate schedule is also serializable and deserializable using keras$optimizers$schedules$serialize and keras$optimizers$schedules$deserialize.


ValueError: if the number of elements in the boundaries and values lists do not match.