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# tf.keras.experimental.CosineDecay

## Class `CosineDecay`

A LearningRateSchedule that uses a cosine decay schedule.

Inherits From: `LearningRateSchedule`

### Aliases:

• Class `tf.compat.v1.keras.experimental.CosineDecay`
• Class `tf.compat.v2.keras.experimental.CosineDecay`

## `__init__`

View source

``````__init__(
initial_learning_rate,
decay_steps,
alpha=0.0,
name=None
)
``````

Applies cosine decay to the learning rate.

See [Loshchilov & Hutter, ICLR2016], SGDR: Stochastic Gradient Descent with Warm Restarts. https://arxiv.org/abs/1608.03983

When training a model, it is often recommended to lower the learning rate as the training progresses. This schedule applies a cosine decay function to an optimizer step, given a provided initial learning rate. It requires a `step` value to compute the decayed learning rate. You can just pass a TensorFlow variable that you increment at each training step.

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):
step = min(step, decay_steps)
cosine_decay = 0.5 * (1 + cos(pi * step / decay_steps))
decayed = (1 - alpha) * cosine_decay + alpha
return initial_learning_rate * decayed
``````

#### Example usage:

``````decay_steps = 1000
lr_decayed_fn = tf.keras.experimental.CosineDecay(
initial_learning_rate, global_step, decay_steps)
``````

You can pass this schedule directly into a `tf.keras.optimizers.Optimizer` as the learning rate. 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. Number of steps to decay over.
• `alpha`: A scalar `float32` or `float64` Tensor or a Python number. Minimum learning rate value as a fraction of initial_learning_rate.
• `name`: String. Optional name of the operation. Defaults to 'CosineDecay'.

#### 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()
``````