tfm.optimization.ExponentialDecayWithOffset

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A LearningRateSchedule that uses an exponential decay schedule.

Inherits From: base_lr_class

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

When training a model, it is often useful to lower the learning rate as the training progresses. This schedule applies an exponential decay function to an optimizer step, given a provided initial learning rate.

The schedule is 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): return initial_learning_rate * decay_rate ^ (step / decay_steps) 

If the argument staircase is True, then step / decay_steps is an integer division and the decayed learning rate follows a staircase function.

You can pass this schedule directly into a tf.keras.optimizers.Optimizer as the learning rate. Example: When fitting a Keras model, decay every 100000 steps with a base of 0.96:

initial_learning_rate = 0.1 lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay( initial_learning_rate, decay_steps=100000, decay_rate=0.96, staircase=True) model.compile(optimizer=tf.keras.optimizers.SGD(learning_rate=lr_schedule), loss='sparse_categorical_crossentropy', metrics=['accuracy']) model.fit(data, labels, epochs=5) 

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 )