Optimizer that implements the RMSprop algorithm

```
optimizer_rmsprop(
learning_rate = 0.001,
rho = 0.9,
momentum = 0,
epsilon = 1e-07,
centered = FALSE,
weight_decay = NULL,
clipnorm = NULL,
clipvalue = NULL,
global_clipnorm = NULL,
use_ema = FALSE,
ema_momentum = 0.99,
ema_overwrite_frequency = 100L,
jit_compile = TRUE,
name = "RMSprop",
...
)
```

Optimizer for use with `compile.keras.engine.training.Model`

.

- learning_rate
Initial value for the learning rate: either a floating point value, or a

`tf.keras.optimizers.schedules.LearningRateSchedule`

instance. Defaults to 0.001.- rho
float, defaults to 0.9. Discounting factor for the old gradients.

- momentum
float, defaults to 0.0. If not 0.0., the optimizer tracks the momentum value, with a decay rate equals to

`1 - momentum`

.- epsilon
A small constant for numerical stability. This epsilon is "epsilon hat" in the Kingma and Ba paper (in the formula just before Section 2.1), not the epsilon in Algorithm 1 of the paper. Defaults to 1e-7.

- centered
Boolean. If

`TRUE`

, gradients are normalized by the estimated variance of the gradient; if FALSE, by the uncentered second moment. Setting this to`TRUE`

may help with training, but is slightly more expensive in terms of computation and memory. Defaults to`FALSE`

.- weight_decay
Float, defaults to NULL. If set, weight decay is applied.

- clipnorm
Float. If set, the gradient of each weight is individually clipped so that its norm is no higher than this value.

- clipvalue
Float. If set, the gradient of each weight is clipped to be no higher than this value.

- global_clipnorm
Float. If set, the gradient of all weights is clipped so that their global norm is no higher than this value.

- use_ema
Boolean, defaults to FALSE. If TRUE, exponential moving average (EMA) is applied. EMA consists of computing an exponential moving average of the weights of the model (as the weight values change after each training batch), and periodically overwriting the weights with their moving average.

- ema_momentum
Float, defaults to 0.99. Only used if

`use_ema=TRUE`

. This is # noqa: E501 the momentum to use when computing the EMA of the model's weights:`new_average = ema_momentum * old_average + (1 - ema_momentum) * current_variable_value`

.- ema_overwrite_frequency
Int or NULL, defaults to NULL. Only used if

`use_ema=TRUE`

. Every`ema_overwrite_frequency`

steps of iterations, we overwrite the model variable by its moving average. If NULL, the optimizer # noqa: E501 does not overwrite model variables in the middle of training, and you need to explicitly overwrite the variables at the end of training by calling`optimizer.finalize_variable_values()`

(which updates the model # noqa: E501 variables in-place). When using the built-in`fit()`

training loop, this happens automatically after the last epoch, and you don't need to do anything.- jit_compile
Boolean, defaults to TRUE. If TRUE, the optimizer will use XLA # noqa: E501 compilation. If no GPU device is found, this flag will be ignored.

- name
String. The name to use for momentum accumulator weights created by the optimizer.

- ...
Used for backward and forward compatibility

The gist of RMSprop is to:

Maintain a moving (discounted) average of the square of gradients

Divide the gradient by the root of this average

This implementation of RMSprop uses plain momentum, not Nesterov momentum.

The centered version additionally maintains a moving average of the gradients, and uses that average to estimate the variance.

Other optimizers:
`optimizer_adadelta()`

,
`optimizer_adagrad()`

,
`optimizer_adamax()`

,
`optimizer_adam()`

,
`optimizer_ftrl()`

,
`optimizer_nadam()`

,
`optimizer_sgd()`