ExponentialDecay class

    initial_learning_rate, decay_steps, decay_rate, staircase=False, name=None

A LearningRateSchedule that uses an exponential decay schedule.

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 tf.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 = tf.keras.optimizers.schedules.ExponentialDecay(


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.


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.