PiecewiseConstantDecay
PiecewiseConstantDecay class
tf_keras.optimizers.schedules.PiecewiseConstantDecay(boundaries, values, name=None)
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 = tf.Variable(0, trainable=False)
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 tf.keras.optimizers.Optimizer
as the learning rate. The learning rate schedule is also serializable and
deserializable using tf.keras.optimizers.schedules.serialize and
tf.keras.optimizers.schedules.deserialize.
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].