Class Adadelta

Inherits From: Optimizer

Defined in tensorflow/python/keras/optimizers.py.

Adadelta optimizer.

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, initial learning rate and decay factor can be set, as in most other Keras optimizers.

It is recommended to leave the parameters of this optimizer at their default values.

Arguments

lr: float >= 0. Initial learning rate, defaults to 1.
    It is recommended to leave it at the default value.
rho: float >= 0. Adadelta decay factor, corresponding to fraction of
    gradient to keep at each time step.
epsilon: float >= 0. Fuzz factor. If `None`, defaults to `K.epsilon()`.
decay: float >= 0. Initial learning rate decay.

References

- [Adadelta - an adaptive learning rate method](http://arxiv.org/abs/1212.5701)

__init__

__init__(
    lr=1.0,
    rho=0.95,
    epsilon=None,
    decay=0.0,
    **kwargs
)

Initialize self. See help(type(self)) for accurate signature.

Methods

tf.keras.optimizers.Adadelta.from_config

from_config(
    cls,
    config
)

tf.keras.optimizers.Adadelta.get_config

get_config()

tf.keras.optimizers.Adadelta.get_gradients

get_gradients(
    loss,
    params
)

Returns gradients of loss with respect to params.

Arguments:

• loss: Loss tensor.
• params: List of variables.

Returns:

List of gradient tensors.

Raises:

• ValueError: In case any gradient cannot be computed (e.g. if gradient function not implemented).

tf.keras.optimizers.Adadelta.get_updates

get_updates(
    loss,
    params
)

tf.keras.optimizers.Adadelta.get_weights

get_weights()

Returns the current value of the weights of the optimizer.

Returns:

A list of numpy arrays.

tf.keras.optimizers.Adadelta.set_weights

set_weights(weights)

Sets the weights of the optimizer, from Numpy arrays.

Should only be called after computing the gradients (otherwise the optimizer has no weights).

Arguments:

• weights: a list of Numpy arrays. The number of arrays and their shape must match number of the dimensions of the weights of the optimizer (i.e. it should match the output of get_weights).

Raises:

• ValueError: in case of incompatible weight shapes.