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A LearningRateSchedule that uses a cosine decay schedule.

Inherits From: `LearningRateSchedule`

tf.keras.experimental.CosineDecay( initial_learning_rate, decay_steps, alpha=0.0, name=None )

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

decay_steps = 1000 lr_decayed_fn = tf.keras.experimental.CosineDecay( 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. |

`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'. |

`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/CosineDecay