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()