tff.learning.optimizers.build_rmsprop

Returns a tff.learning.optimizers.Optimizer for RMSprop.

tff.learning.optimizers.build_rmsprop(
    learning_rate: optimizer.Float,
    decay: optimizer.Float = 0.9,
    epsilon: optimizer.Float = 1e-07
) -> tff.learning.optimizers.Optimizer

The RMSprop optimizer is based on Tieleman and Hinton, 2012.

The update rule given learning rate lr, epsilon eps, decay d, preconditioner s, weights w and gradients g is:

s = d * s + (1 - d) * g**2
w = w - lr * g / (sqrt(s) + eps)

Args
learning_rate	A positive float for learning rate, default to 0.01.
decay	A float between 0.0 and 1.0 for the decay used to track the magnitude of previous gradients.
epsilon	A small non-negative float, used to maintain numerical stability.