RMSProp optimizer

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

learning_rate | float >= 0. Learning rate. |
---|---|

rho | float >= 0. Decay factor. |

epsilon | float >= 0. Fuzz factor. If |

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.

Other optimizers:
`optimizer_adadelta()`

,
`optimizer_adagrad()`

,
`optimizer_adamax()`

,
`optimizer_adam()`

,
`optimizer_nadam()`

,
`optimizer_sgd()`