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tf.keras.optimizers.Adadelta

Optimizer that implements the Adadelta algorithm.

Inherits From: Optimizer

tf.keras.optimizers.Adadelta(
    learning_rate=0.001,
    rho=0.95,
    epsilon=1e-07,
    weight_decay=None,
    clipnorm=None,
    clipvalue=None,
    global_clipnorm=None,
    use_ema=False,
    ema_momentum=0.99,
    ema_overwrite_frequency=None,
    loss_scale_factor=None,
    gradient_accumulation_steps=None,
    name='adadelta',
    **kwargs
)

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.

Args
`learning_rate`	A float, a `keras.optimizers.schedules.LearningRateSchedule` instance, or a callable that takes no arguments and returns the actual value to use. The learning rate. 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 floating point value. The decay rate. Defaults to `0.95`.
`epsilon`	Small floating point value for maintaining numerical stability.
`name`	String. The name to use for momentum accumulator weights created by the optimizer.
`weight_decay`	Float. 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 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 None, defaults to None. Only used if `use_ema=True`. Every `ema_overwrite_frequency` steps of iterations, we overwrite the model variable by its moving average. If None, the optimizer 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 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.
`loss_scale_factor`	Float or `None`. If a float, the scale factor will be multiplied the loss before computing gradients, and the inverse of the scale factor will be multiplied by the gradients before updating variables. Useful for preventing underflow during mixed precision training. Alternately, `keras.optimizers.LossScaleOptimizer` will automatically set a loss scale factor.
`gradient_accumulation_steps`	Int or `None`. If an int, model & optimizer variables will not be updated at every step; instead they will be updated every `gradient_accumulation_steps` steps, using the average value of the gradients since the last update. This is known as "gradient accumulation". This can be useful when your batch size is very small, in order to reduce gradient noise at each update step.

Reference:

Zeiler, 2012

Attributes
`learning_rate`
`variables`

Args
`variable`	The variable to update.
`value`	The value to add to the variable.

Args
`var_list`	A list of `Variable`s to exclude from weight decay.
`var_names`	A list of strings. If any string in `var_names` appear in the model variable's name, then this model variable is excluded from weight decay. For example, `var_names=['bias']` excludes all bias variables from weight decay.

Args
`config`	A Python dictionary, typically the output of get_config.
`custom_objects`	A Python dictionary mapping names to additional user-defined Python objects needed to recreate this optimizer.

tf.keras.optimizers.Adadelta

Reference:

Methods

add_variable

add_variable_from_reference

apply

apply_gradients

assign

assign_add

assign_sub

build

exclude_from_weight_decay

finalize_variable_values

from_config

get_config

load_own_variables

save_own_variables

scale_loss

set_weights

stateless_apply

update_step

`add_variable`

`add_variable_from_reference`

`apply`

`apply_gradients`

`assign`

`assign_add`

`assign_sub`

`build`

`exclude_from_weight_decay`

`finalize_variable_values`

`from_config`

`get_config`

`load_own_variables`

`save_own_variables`

`scale_loss`

`set_weights`

`stateless_apply`

`update_step`