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A LearningRateSchedule that uses an exponential decay schedule.

Inherits From: LearningRateSchedule

When training a model, it is often recommended 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 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(

              metrics=['accuracy']), 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.

initial_learning_rate A scalar float32 or float64 Tensor or a Python number. The initial learning rate.
decay_steps A scalar int32 or int64 Tensor or a Python number. Must be positive. See the decay computation above.
decay_rate A scalar float32 or float64 Tensor or a Python number. The decay rate.
staircase Boolean. If True decay the learning rate at discrete intervals
name String. Optional name of the operation. Defaults to 'ExponentialDecay'.



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Instantiates a LearningRateSchedule from its config.

config Output of get_config().

A LearningRateSchedule instance.


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Call self as a function.