View source on GitHub |
Piecewise constant from boundaries and interval values.
tf.compat.v1.train.piecewise_constant(
x, boundaries, values, name=None
)
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.
global_step = tf.Variable(0, trainable=False)
boundaries = [100000, 110000]
values = [1.0, 0.5, 0.1]
learning_rate = tf.compat.v1.train.piecewise_constant(global_step, boundaries,
values)
# Later, whenever we perform an optimization step, we increment global_step.
Returns | |
---|---|
A 0-D Tensor. Its value is values[0] when x <= boundaries[0] ,
values[1] when x > boundaries[0] and x <= boundaries[1] , ...,
and values[-1] when x > boundaries[-1] .
|
Raises | |
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ValueError
|
if types of x and boundaries do not match, or types of all
values do not match or
the number of elements in the lists does not match.
|
eager compatibility
When eager execution is enabled, this function returns a function which in turn returns the decayed learning rate Tensor. This can be useful for changing the learning rate value across different invocations of optimizer functions.