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Applies a polynomial decay to the learning rate.
tf.compat.v1.train.polynomial_decay(
learning_rate,
global_step,
decay_steps,
end_learning_rate=0.0001,
power=1.0,
cycle=False,
name=None
)
It is commonly observed that a monotonically decreasing learning rate, whose
degree of change is carefully chosen, results in a better performing model.
This function applies a polynomial decay function to a provided initial
learning_rate
to reach an end_learning_rate
in the given decay_steps
.
It requires a global_step
value to compute the decayed learning rate. You
can just pass a TensorFlow variable that you increment at each training step.
The function returns the decayed learning rate. It is computed as:
global_step = min(global_step, decay_steps)
decayed_learning_rate = (learning_rate - end_learning_rate) *
(1 - global_step / decay_steps) ^ (power) +
end_learning_rate
If cycle
is True then a multiple of decay_steps
is used, the first one
that is bigger than global_steps
.
decay_steps = decay_steps * ceil(global_step / decay_steps)
decayed_learning_rate = (learning_rate - end_learning_rate) *
(1 - global_step / decay_steps) ^ (power) +
end_learning_rate
Example: decay from 0.1 to 0.01 in 10000 steps using sqrt (i.e. power=0.5):
...
global_step = tf.Variable(0, trainable=False)
starter_learning_rate = 0.1
end_learning_rate = 0.01
decay_steps = 10000
learning_rate = tf.compat.v1.train.polynomial_decay(starter_learning_rate,
global_step,
decay_steps, end_learning_rate,
power=0.5)
# Passing global_step to minimize() will increment it at each step.
learning_step = (
tf.compat.v1.train.GradientDescentOptimizer(learning_rate)
.minimize(...my loss..., global_step=global_step)
)
Returns | |
---|---|
A scalar Tensor of the same type as learning_rate . The decayed
learning rate.
|
Raises | |
---|---|
ValueError
|
if global_step is not supplied.
|
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.