A LearningRateSchedule that uses an inverse time decay schedule

  staircase = FALSE,
  name = NULL



A scalar float32 or float64 Tensor or an R number. The initial learning rate.


A scalar int32 or int64 Tensor or an R number. How often to apply decay.


An R number. The decay rate.


Boolean. Whether to apply decay in a discrete staircase, as opposed to continuous, fashion.


For backwards and forwards compatibility


String. Optional name of the operation. Defaults to 'InverseTimeDecay'.


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:

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 keras 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 <- 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)