InverseTimeDecay class


A LearningRateSchedule that uses an inverse time decay schedule.

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 backend 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 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)

              metrics=['accuracy']), labels, epochs=5)


  • initial_learning_rate: A Python float. The initial learning rate.
  • decay_steps: How often to apply decay.
  • decay_rate: A Python number. The decay rate.
  • staircase: Whether to apply decay in a discrete staircase, as o pposed to continuous, fashion.
  • name: String. Optional name of the operation. Defaults to "InverseTimeDecay".


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.