tf.keras.experimental.CosineDecay

A LearningRateSchedule that uses a cosine decay schedule.

Inherits From: LearningRateSchedule

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Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.keras.experimental.CosineDecay

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

Example usage:

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.

Methods

from_config

View source

@classmethod
from_config(
    config
)

Instantiates a LearningRateSchedule from its config.

get_config

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get_config()

__call__

View source

__call__(
    step
)

Call self as a function.