tf.keras.optimizers.schedules.ExponentialDecay

A LearningRateSchedule that uses an exponential decay schedule.

Inherits From: LearningRateSchedule

tf.keras.optimizers.schedules.ExponentialDecay(
    initial_learning_rate,
    decay_steps,
    decay_rate,
    staircase=False,
    name='ExponentialDecay'
)

Used in the notebooks

Used in the guide

When training a model, it is often useful to lower the learning rate as the training progresses. This schedule applies an exponential decay function to an optimizer step, given a provided initial learning rate.

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):
    return initial_learning_rate * decay_rate ^ (step / decay_steps)

If the argument staircase is True, then step / decay_steps is an integer division and the decayed learning rate follows a staircase function.

You can pass this schedule directly into a keras.optimizers.Optimizer as the learning rate. Example: When fitting a Keras model, decay every 100000 steps with a base of 0.96:

initial_learning_rate = 0.1
lr_schedule = keras.optimizers.schedules.ExponentialDecay(
    initial_learning_rate,
    decay_steps=100000,
    decay_rate=0.96,
    staircase=True)

model.compile(optimizer=keras.optimizers.SGD(learning_rate=lr_schedule),
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

model.fit(data, labels, epochs=5)

The learning rate schedule is also serializable and deserializable using keras.optimizers.schedules.serialize and keras.optimizers.schedules.deserialize.

initial_learning_rate A Python float. The initial learning rate.
decay_steps A Python integer. Must be positive. See the decay computation above.
decay_rate A Python float. The decay rate.
staircase Boolean. If True decay the learning rate at discrete intervals.
name String. Optional name of the operation. Defaults to "ExponentialDecay".

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.

Methods

from_config

View source

@classmethod
from_config(
    config
)

Instantiates a LearningRateSchedule from its config.

Args
config Output of get_config().

Returns
A LearningRateSchedule instance.

get_config

View source

get_config()

__call__

View source

__call__(
    step
)

Call self as a function.