PiecewiseConstantDecay
PiecewiseConstantDecay class
keras.optimizers.schedules.PiecewiseConstantDecay(
boundaries, values, name="PiecewiseConstant"
)
A LearningRateSchedule that uses a piecewise constant decay schedule.
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 = ops.array(0)
boundaries = [100000, 110000]
values = [1.0, 0.5, 0.1]
learning_rate_fn = keras.optimizers.schedules.PiecewiseConstantDecay(
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.optimizers.Optimizer
as the learning rate. The learning rate schedule is also serializable and
deserializable using keras.optimizers.schedules.serialize and
keras.optimizers.schedules.deserialize.
Arguments
- boundaries: A list of Python numbers with strictly increasing entries, and with all elements having the same type as the optimizer step.
- values: A list of Python numbers that specifies the values for the
intervals defined by
boundaries. It should have one more element thanboundaries, and all elements should have the same type. - name: A string. Optional name of the operation. Defaults to
"PiecewiseConstant".
Returns
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].
Raises
- ValueError: if the number of elements in the
boundariesandvalueslists do not match.