RMSProp optimizer

optimizer_rmsprop(
  learning_rate = 0.001,
  rho = 0.9,
  epsilon = NULL,
  decay = 0,
  clipnorm = NULL,
  clipvalue = NULL,
  ...
)

Arguments

learning_rate	float >= 0. Learning rate.
rho	float >= 0. Decay factor.
epsilon	float >= 0. Fuzz factor. If `NULL`, defaults to `k_epsilon()`.
decay	float >= 0. Learning rate decay over each update.
clipnorm	Gradients will be clipped when their L2 norm exceeds this value.
clipvalue	Gradients will be clipped when their absolute value exceeds this value.
...	Unused, present only for backwards compatability

It is recommended to leave the parameters of this optimizer at their default values (except the learning rate, which can be freely tuned).

This optimizer is usually a good choice for recurrent neural networks.