tfm.optimization.CosineDecayWithOffset

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A LearningRateSchedule that uses a cosine decay with optional warmup.

Inherits From: base_lr_class

tfm.optimization.CosineDecayWithOffset( offset=0, **kwargs ) 

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 TensorFlow 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 = tf.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 = tf.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 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.

Child Classes

class base_lr_class

Methods

from_config

@classmethod from_config( config ) 

Instantiates a LearningRateSchedule from its config.

get_config

get_config() 

__call__

View source

__call__( step )