A LearningRateSchedule that uses an inverse time decay schedule. — learning_rate_schedule_inverse_time

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:

decayed_learning_rate <- function(step) {
  initial_learning_rate / (1 + decay_rate * step / decay_step)
}

or, if staircase is TRUE, as:

decayed_learning_rate <- function(step) {
  initial_learning_rate /
           (1 + decay_rate * floor(step / decay_step))
}

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

Usage

learning_rate_schedule_inverse_time_decay(
  initial_learning_rate,
  decay_steps,
  decay_rate,
  staircase = FALSE,
  name = "InverseTimeDecay"
)

Arguments

initial_learning_rate: A float. The initial learning rate.
decay_steps: How often to apply decay.
decay_rate: A 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".

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 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 <- learning_rate_schedule_inverse_time_decay(
    initial_learning_rate, decay_steps, decay_rate)

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

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

A `LearningRateSchedule` that uses an inverse time decay schedule.

Usage

Arguments

Value

Examples

See also