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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)
)
Args | |
|---|---|
learning_rate
|
A scalar float32 or float64 Tensor or a Python number.
The initial learning rate.
|
global_step
|
A scalar int32 or int64 Tensor or a Python number. Global
step to use for the decay computation. Must not be negative.
|
decay_steps
|
A scalar int32 or int64 Tensor or a Python number. Must
be positive. See the decay computation above.
|
end_learning_rate
|
A scalar float32 or float64 Tensor or a Python
number. The minimal end learning rate.
|
power
|
A scalar float32 or float64 Tensor or a Python number. The
power of the polynomial. Defaults to linear, 1.0.
|
cycle
|
A boolean, whether or not it should cycle beyond decay_steps. |
name
|
String. Optional name of the operation. Defaults to 'PolynomialDecay'. |
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.