PolynomialDecay
PolynomialDecay class
tf_keras.optimizers.schedules.PolynomialDecay(
initial_learning_rate,
decay_steps,
end_learning_rate=0.0001,
power=1.0,
cycle=False,
name=None,
)
A LearningRateSchedule that uses a polynomial decay schedule.
It is commonly observed that a monotonically decreasing learning rate, whose
degree of change is carefully chosen, results in a better performing model.
This schedule applies a polynomial decay function to an optimizer step,
given a provided initial_learning_rate, to reach an end_learning_rate
in the given decay_steps.
It requires a step value to compute the decayed learning rate. You
can just pass a TensorFlow variable that you increment at each training
step.
The schedule is a 1-arg callable that produces a decayed learning rate when passed the current optimizer step. This can be useful for changing the learning rate value across different invocations of optimizer functions. It is computed as:
def decayed_learning_rate(step):
step = min(step, decay_steps)
return ((initial_learning_rate - end_learning_rate) *
(1 - 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 step.
def decayed_learning_rate(step):
decay_steps = decay_steps * ceil(step / decay_steps)
return ((initial_learning_rate - end_learning_rate) *
(1 - step / decay_steps) ^ (power)
) + end_learning_rate
You can pass this schedule directly into a tf.keras.optimizers.Optimizer
as the learning rate.
Example
Fit a model while decaying from 0.1 to 0.01 in 10000 steps using
sqrt (i.e. power=0.5):
...
starter_learning_rate = 0.1
end_learning_rate = 0.01
decay_steps = 10000
learning_rate_fn = tf.keras.optimizers.schedules.PolynomialDecay(
starter_learning_rate,
decay_steps,
end_learning_rate,
power=0.5)
model.compile(optimizer=tf.keras.optimizers.SGD(
learning_rate=learning_rate_fn),
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model.fit(data, labels, epochs=5)
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 initial_learning_rate.