# optimizer_rmsprop

0th

Percentile

##### RMSProp optimizer

RMSProp optimizer

##### Usage
optimizer_rmsprop(lr = 0.001, rho = 0.9, epsilon = NULL, decay = 0,
clipnorm = NULL, clipvalue = NULL)
##### Arguments
lr

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.

##### 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.

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