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tf.keras.optimizers.schedules.ExponentialDecay

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Class ExponentialDecay

A LearningRateSchedule that uses an exponential decay schedule.

Inherits From: LearningRateSchedule

Aliases:

  • Class tf.compat.v1.keras.optimizers.schedules.ExponentialDecay
  • Class tf.compat.v2.keras.optimizers.schedules.ExponentialDecay
  • Class tf.compat.v2.optimizers.schedules.ExponentialDecay
  • Class tf.optimizers.schedules.ExponentialDecay

Used in the guide:

__init__

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__init__(
    initial_learning_rate,
    decay_steps,
    decay_rate,
    staircase=False,
    name=None
)

Applies exponential decay to the learning rate.

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(
    initial_learning_rate,
    decay_steps=100000,
    decay_rate=0.96,
    staircase=True)

model.compile(optimizer=tf.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 tf.keras.optimizers.schedules.serialize and tf.keras.optimizers.schedules.deserialize.

Args:

  • 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'.

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.

Methods

__call__

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__call__(step)

Call self as a function.

from_config

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from_config(
    cls,
    config
)

Instantiates a LearningRateSchedule from its config.

Args:

  • config: Output of get_config().

Returns:

A LearningRateSchedule instance.

get_config

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get_config()