A LearningRateSchedule that uses a cosine decay schedule


A LearningRateSchedule that uses a cosine decay schedule


  alpha = 0, 
  name = NULL 


Arguments Description
initial_learning_rate A scalar float32 or float64 Tensor or a R number. The initial learning rate.
decay_steps A scalar int32 or int64 Tensor or an R number. Number of steps to decay over.
alpha A scalar float32 or float64 Tensor or an R number. Minimum learning rate value as a fraction of initial_learning_rate.
For backwards and forwards compatibility
name String. Optional name of the operation. Defaults to ‘CosineDecay’.


See Loshchilov & Hutter, ICLR2016, SGDR: Stochastic Gradient Descent with Warm Restarts. When training a model, it is often useful to lower the learning rate as the training progresses. This schedule applies a cosine 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) {

step <- min(step, decay_steps)

cosine_decay = <- 0.5 * (1 + cos(pi * step / decay_steps))

decayed <- (1 - alpha) * cosine_decay + alpha

initial_learning_rate * decayed


Example usage:

decay_steps <- 1000

lr_decayed_fn <-

learning_rate_schedule_cosine_decay(initial_learning_rate, decay_steps)

``You can pass this schedule directly into a keras Optimizer as thelearning_rate`.

See Also