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# tf.keras.experimental.NoisyLinearCosineDecay

A LearningRateSchedule that uses a noisy linear cosine decay schedule.

Inherits From: `LearningRateSchedule`

See [Bello et al., ICML2017] Neural Optimizer Search with RL. https://arxiv.org/abs/1709.07417

For the idea of warm starts here controlled by `num_periods`, see [Loshchilov & Hutter, ICLR2016] SGDR: Stochastic Gradient Descent with Warm Restarts. https://arxiv.org/abs/1608.03983

Note that linear cosine decay is more aggressive than cosine decay and larger initial learning rates can typically be used.

When training a model, it is often recommended to lower the learning rate as the training progresses. This schedule applies a noisy linear 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)
linear_decay = (decay_steps - step) / decay_steps)
cosine_decay = 0.5 * (
1 + cos(pi * 2 * num_periods * step / decay_steps))
decayed = (alpha + linear_decay + eps_t) * cosine_decay + beta
return initial_learning_rate * decayed
``````

where eps_t is 0-centered gaussian noise with variance initial_variance / (1 + global_step) ** variance_decay

#### Example usage:

``````decay_steps = 1000
lr_decayed_fn = (
tf.keras.experimental.NoisyLinearCosineDecay(
initial_learning_rate, 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`.

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. Number of steps to decay over.
`initial_variance` initial variance for the noise. See computation above.
`variance_decay` decay for the noise's variance. See computation above.
`num_periods` Number of periods in the cosine part of the decay. See computation above.
`alpha` See computation above.
`beta` See computation above.
`name` String. Optional name of the operation. Defaults to 'NoisyLinearCosineDecay'.

## Methods

### `from_config`

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Instantiates a `LearningRateSchedule` from its config.

Args
`config` Output of `get_config()`.

Returns
A `LearningRateSchedule` instance.

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### `__call__`

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Call self as a function.