tf.keras.optimizers.schedules.CosineDecay

A LearningRateSchedule that uses a cosine decay with optional warmup.

Inherits From: LearningRateSchedule

tf.keras.optimizers.schedules.CosineDecay( initial_learning_rate, decay_steps, alpha=0.0, name='CosineDecay', warmup_target=None, warmup_steps=0 )

See Loshchilov & Hutter, ICLR2016, SGDR: Stochastic Gradient Descent with Warm Restarts.

For the idea of a linear warmup of our learning rate, see Goyal et al..

When we begin training a model, we often want an initial increase in our learning rate followed by a decay. If warmup_target is an int, this schedule applies a linear increase per optimizer step to our learning rate from initial_learning_rate to warmup_target for a duration of warmup_steps. Afterwards, it applies a cosine decay function taking our learning rate from warmup_target to alpha for a duration of decay_steps. If warmup_target is None we skip warmup and our decay will take our learning rate from initial_learning_rate to alpha. It requires a step value to compute the learning rate. You can just pass a backend variable that you increment at each training step.

The schedule is a 1-arg callable that produces a warmup followed by 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.

Our warmup is computed as:

def warmup_learning_rate(step): completed_fraction = step / warmup_steps total_delta = target_warmup - initial_learning_rate return completed_fraction * total_delta

And our decay is computed as:

if warmup_target is None: initial_decay_lr = initial_learning_rate else: initial_decay_lr = warmup_target 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_decay_lr * decayed

Example usage without warmup:

decay_steps = 1000 initial_learning_rate = 0.1 lr_decayed_fn = keras.optimizers.schedules.CosineDecay( initial_learning_rate, decay_steps)

Example usage with warmup:

decay_steps = 1000 initial_learning_rate = 0 warmup_steps = 1000 target_learning_rate = 0.1 lr_warmup_decayed_fn = keras.optimizers.schedules.CosineDecay( initial_learning_rate, decay_steps, warmup_target=target_learning_rate, warmup_steps=warmup_steps )

You can pass this schedule directly into a keras.optimizers.Optimizer as the learning rate. The learning rate schedule is also serializable and deserializable using keras.optimizers.schedules.serialize and keras.optimizers.schedules.deserialize.

initial_learning_rate A Python float. The initial learning rate.
decay_steps A Python int. Number of steps to decay over.
alpha A Python float. Minimum learning rate value for decay as a fraction of initial_learning_rate.
name String. Optional name of the operation. Defaults to "CosineDecay".
warmup_target A Python float. The target learning rate for our warmup phase. Will cast to the initial_learning_rate datatype. Setting to None will skip warmup and begins decay phase from initial_learning_rate. Otherwise scheduler will warmup from initial_learning_rate to warmup_target.
warmup_steps A Python int. Number of steps to warmup over.

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

from_config

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@classmethod from_config( config )

Instantiates a LearningRateSchedule from its config.

Args
config Output of get_config().

Returns
A LearningRateSchedule instance.

get_config

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get_config()

__call__

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__call__( step )

Call self as a function.