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Keras API reference /
Optimizers /
Adadelta

`Adadelta`

class```
tf.keras.optimizers.Adadelta(
learning_rate=0.001, rho=0.95, epsilon=1e-07, name="Adadelta", **kwargs
)
```

Optimizer that implements the Adadelta algorithm.

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.

**Arguments**

**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.**epsilon**: Small floating point value used to maintain numerical stability.**name**: Optional name prefix for the operations created when applying gradients. Defaults to`"Adadelta"`

.****kwargs**: keyword arguments. Allowed arguments are`clipvalue`

,`clipnorm`

,`global_clipnorm`

. If`clipvalue`

(float) is set, the gradient of each weight is clipped to be no higher than this value. If`clipnorm`

(float) is set, the gradient of each weight is individually clipped so that its norm is no higher than this value. If`global_clipnorm`

(float) is set the gradient of all weights is clipped so that their global norm is no higher than this value.

**Reference**