tfm.optimization.ExponentialDecayWithOffset.base_lr_class

A LearningRateSchedule that uses an exponential decay schedule.

tfm.optimization.ExponentialDecayWithOffset.base_lr_class(
    initial_learning_rate, decay_steps, decay_rate, staircase=False, name=None
)

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.

Methods

from_config

@classmethod
from_config(
    config
)

Instantiates a LearningRateSchedule from its config.

get_config

get_config()

__call__

__call__(
    step
)

Call self as a function.