Optimizer that implements the Adadelta algorithm

```
optimizer_adadelta(
learning_rate = 0.001,
rho = 0.95,
epsilon = 1e-07,
weight_decay = NULL,
clipnorm = NULL,
clipvalue = NULL,
global_clipnorm = NULL,
use_ema = FALSE,
ema_momentum = 0.99,
ema_overwrite_frequency = NULL,
jit_compile = TRUE,
name = "Adadelta",
...
)
```

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. Note that`Adadelta`

tends to benefit from higher initial learning rate values compared to other optimizers. To match the exact form in the original paper, use 1.0.- rho
A

`Tensor`

or a floating point value. The decay rate. Defaults to 0.95.- epsilon
Small floating point value used to maintain numerical stability. Defaults to 1e-7.

- 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

Adadelta optimization is a stochastic gradient descent method that is based on adaptive learning rate per dimension to address two drawbacks:

The continual decay of learning rates throughout training.

The need for a manually selected global learning rate.

Adadelta is a more robust extension of Adagrad that adapts learning rates based on a moving window of gradient updates, instead of accumulating all past gradients. This way, Adadelta continues learning even when many updates have been done. Compared to Adagrad, in the original version of Adadelta you don't have to set an initial learning rate. In this version, the initial learning rate can be set, as in most other Keras optimizers.

Other optimizers:
`optimizer_adagrad()`

,
`optimizer_adamax()`

,
`optimizer_adam()`

,
`optimizer_ftrl()`

,
`optimizer_nadam()`

,
`optimizer_rmsprop()`

,
`optimizer_sgd()`