ExponentialDecay
ExponentialDecay class
tf_keras.optimizers.schedules.ExponentialDecay(
initial_learning_rate, decay_steps, decay_rate, staircase=False, name=None
)
A LearningRateSchedule that uses an exponential decay schedule.
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 TF-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.
Returns
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.