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A LearningRateSchedule that uses a noisy linear cosine decay schedule.

Inherits From: `LearningRateSchedule`

tf.keras.experimental.NoisyLinearCosineDecay( initial_learning_rate, decay_steps, initial_variance=1.0, variance_decay=0.55, num_periods=0.5, alpha=0.0, beta=0.001, name=None )

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

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`

.

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` . |

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. |

`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'. |

`from_config`

@classmethod from_config( config )

Instantiates a `LearningRateSchedule`

from its config.

Args | |
---|---|

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

Returns | |
---|---|

A `LearningRateSchedule` instance. |

`get_config`

get_config()

`__call__`

__call__( step )

Call self as a function.

© 2020 The TensorFlow Authors. All rights reserved.

Licensed under the Creative Commons Attribution License 3.0.

Code samples licensed under the Apache 2.0 License.

https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/keras/experimental/NoisyLinearCosineDecay