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# tf.keras.optimizers.schedules.ExponentialDecay

## 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`

## `__init__`

View source

``````__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__`

View source

``````__call__(step)
``````

### `from_config`

View source

``````from_config(
cls,
config
)
``````

Instantiates a `LearningRateSchedule` from its config.

#### Args:

• `config`: Output of `get_config()`.

#### Returns:

A `LearningRateSchedule` instance.

### `get_config`

View source

``````get_config()
``````