tf.keras.optimizers.schedules.InverseTimeDecay

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A LearningRateSchedule that uses an inverse time decay schedule.

Inherits From: LearningRateSchedule

tf.keras.optimizers.schedules.InverseTimeDecay(
    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 the inverse decay function to an optimizer step, given a provided initial learning rate. It requires a step value to compute the decayed 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 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 / (1 + decay_rate * step / decay_step)

or, if staircase is True, as:

def decayed_learning_rate(step):
  return initial_learning_rate / (1 + decay_rate * floor(step / decay_step))

You can pass this schedule directly into a tf.keras.optimizers.Optimizer as the learning rate. Example: Fit a Keras model when decaying 1/t with a rate of 0.5:

...
initial_learning_rate = 0.1
decay_steps = 1.0
decay_rate = 0.5
learning_rate_fn = keras.optimizers.schedules.InverseTimeDecay(
  initial_learning_rate, decay_steps, decay_rate)

model.compile(optimizer=tf.keras.optimizers.SGD(
                  learning_rate=learning_rate_fn),
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

model.fit(data, labels, epochs=5)

Methods

from_config

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@classmethod
from_config(
    config
)

Instantiates a LearningRateSchedule from its config.

get_config

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get_config()

__call__

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__call__(
    step
)

Call self as a function.