class RMSprop(learning_rate: float | Callable[[array], array], alpha: float = 0.99, eps: float = 1e-08)#

The RMSprop optimizer [1].

[1]: Tieleman, T. and Hinton, G. 2012. Lecture 6.5-rmsprop, coursera: Neural networks for machine learning

\[\begin{split}v_{t+1} &= \alpha v_t + (1 - \alpha) g_t^2 \\ w_{t+1} &= w_t - \lambda \frac{g_t}{\sqrt{v_{t+1}} + \epsilon}\end{split}\]
  • learning_rate (float or callable) – The learning rate \(\lambda\).

  • alpha (float, optional) – The smoothing constant \(\alpha\). Default: 0.99

  • eps (float, optional) – The term \(\epsilon\) added to the denominator to improve numerical stability. Default: 1e-8


__init__(learning_rate[, alpha, eps])

apply_single(gradient, parameter, state)

Performs the RMSprop parameter update and stores \(v\) in the optimizer state.

init_single(parameter, state)

Initialize optimizer state