A LearningRateSchedule that uses a polynomial decay schedule. — learning_rate_schedule_polynomial

It is commonly observed that a monotonically decreasing learning rate, whose degree of change is carefully chosen, results in a better performing model. This schedule applies a polynomial decay function to an optimizer step, given a provided initial_learning_rate, to reach an end_learning_rate in the given decay_steps.

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:

decayed_learning_rate <- function(step) {
  step = min(step, decay_steps)
  ((initial_learning_rate - end_learning_rate) *
    (1 - step / decay_steps) ^ (power)) +
    end_learning_rate
}

If cycle is TRUE then a multiple of decay_steps is used, the first one that is bigger than step.

decayed_learning_rate <- function(step) {
  decay_steps = decay_steps * ceil(step / decay_steps)
  ((initial_learning_rate - end_learning_rate) *
      (1 - step / decay_steps) ^ (power)) +
    end_learning_rate
}

You can pass this schedule directly into a Optimizer as the learning rate.

Usage

learning_rate_schedule_polynomial_decay(
  initial_learning_rate,
  decay_steps,
  end_learning_rate = 1e-04,
  power = 1,
  cycle = FALSE,
  name = "PolynomialDecay"
)

Arguments

initial_learning_rate: A float. The initial learning rate.
decay_steps: A integer. Must be positive. See the decay computation above.
end_learning_rate: A float. The minimal end learning rate.
power: A float. The power of the polynomial. Defaults to 1.0.
cycle: A boolean, whether it should cycle beyond decay_steps.
name: String. Optional name of the operation. Defaults to "PolynomialDecay".

Value

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.

Examples

Fit a model while decaying from 0.1 to 0.01 in 10000 steps using sqrt (i.e. power=0.5):

...
starter_learning_rate <- 0.1
end_learning_rate <- 0.01
decay_steps <- 10000
learning_rate_fn <- learning_rate_schedule_polynomial_decay(
    starter_learning_rate,
    decay_steps,
    end_learning_rate,
    power=0.5)

model %>% compile(
  optimizer = optimizer_sgd(learning_rate=learning_rate_fn),
  loss = 'sparse_categorical_crossentropy',
  metrics = 'accuracy'
)

model %>% fit(data, labels, epochs=5)

The learning rate schedule is also serializable and deserializable using keras$optimizers$schedules$serialize and keras$optimizers$schedules$deserialize.

A `LearningRateSchedule` that uses a polynomial decay schedule.

Usage

Arguments

Value

Examples

See also