A LearningRateSchedule that uses a cosine decay schedule
learning_rate_schedule_cosine_decay(
initial_learning_rate,
decay_steps,
alpha = 0,
...,
name = NULL
)A scalar float32 or float64 Tensor or a
R number. The initial learning rate.
A scalar int32 or int64 Tensor or an R number.
Number of steps to decay over.
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
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 the learning_rate.