R/optimizers.R

optimizer_rmsprop

RMSProp optimizer

Description

RMSProp optimizer

Usage

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

Arguments

Arguments Description
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

Note

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.

See Also

Other optimizers: optimizer_adadelta(), optimizer_adagrad(), optimizer_adamax(), optimizer_adam(), optimizer_nadam(), optimizer_sgd()