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## Class Adadelta

Inherits From: Optimizer

### Aliases:

• Class tf.compat.v1.keras.optimizers.Adadelta
• Class tf.compat.v2.keras.optimizers.Adadelta
• Class tf.compat.v2.optimizers.Adadelta
• Class tf.optimizers.Adadelta

Adadelta optimization is a stochastic gradient descent method that is based on adaptive learning rate per dimension to address two drawbacks: 1) the continual decay of learning rates throughout training 2) the need for a manually selected global learning rate

Two accumulation steps are required: 1) the accumulation of gradients squared, 2) the accumulation of updates squared.

#### Initialization:

$$E[g^2]_0 := 0 \text{(Initialize gradient 2nd order moment vector)}$$

$$E[\Delta x^2]_0 := 0 \text{(Initialize 2nd order variable update)}$$

$$t := t + 1$$

$$E[g^2]t := \rho * E[g^2]{t-1} + (1 - \rho) * g^2$$

$$\Delta xt = -RMS[\Delta x]{t-1} * g_t / RMS[g]_t$$

$$E[\Delta x^2]t := \rho * E[\Delta x^2]{t-1} + (1 - \rho) * \Delta x_t^2$$

$$xt := x{t-1} + \Delta x_{t}$$

References See M. D. Zeiler (pdf)

## __init__

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__init__(
learning_rate=0.001,
rho=0.95,
epsilon=1e-07,
**kwargs
)


#### Args:

• learning_rate: A Tensor or a floating point value. The learning rate. To match the exact form in the original paper use 1.0.
• rho: A Tensor or a floating point value. The decay rate.
• epsilon: A Tensor or a floating point value. A constant epsilon used
   to better conditioning the grad update.

• name: Optional name prefix for the operations created when applying gradients. Defaults to "Adadelta".
• **kwargs: keyword arguments. Allowed to be {clipnorm, clipvalue, lr, decay}. clipnorm is clip gradients by norm; clipvalue is clip gradients by value, decay is included for backward compatibility to allow time inverse decay of learning rate. lr is included for backward compatibility, recommended to use learning_rate instead.

#### Eager Compatibility

When eager execution is enabled, learning_rate, rho, and epsilon can each be a callable that takes no arguments and returns the actual value to use. This can be useful for changing these values across different invocations of optimizer functions.

## Properties

### iterations

Variable. The number of training steps this Optimizer has run.

### weights

Returns variables of this Optimizer based on the order created.

## Methods

### add_slot

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add_slot(
var,
slot_name,
initializer='zeros'
)


Add a new slot variable for var.

### add_weight

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add_weight(
name,
shape,
dtype=None,
initializer='zeros',
trainable=None,
synchronization=tf.VariableSynchronization.AUTO,
aggregation=tf.compat.v1.VariableAggregation.NONE
)


### apply_gradients

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apply_gradients(
name=None
)


This is the second part of minimize(). It returns an Operation that applies gradients.

#### Args:

• grads_and_vars: List of (gradient, variable) pairs.
• name: Optional name for the returned operation. Default to the name passed to the Optimizer constructor.

#### Returns:

An Operation that applies the specified gradients. If global_step was not None, that operation also increments global_step.

#### Raises:

• TypeError: If grads_and_vars is malformed.
• ValueError: If none of the variables have gradients.

### from_config

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from_config(
cls,
config,
custom_objects=None
)


Creates an optimizer from its config.

This method is the reverse of get_config, capable of instantiating the same optimizer from the config dictionary.

#### Arguments:

• config: A Python dictionary, typically the output of get_config.
• custom_objects: A Python dictionary mapping names to additional Python objects used to create this optimizer, such as a function used for a hyperparameter.

#### Returns:

An optimizer instance.

### get_config

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get_config()


Returns the config of the optimimizer.

An optimizer config is a Python dictionary (serializable) containing the configuration of an optimizer. The same optimizer can be reinstantiated later (without any saved state) from this configuration.

#### Returns:

Python dictionary.

### get_gradients

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get_gradients(
loss,
params
)


Returns gradients of loss with respect to params.

#### Arguments:

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

#### Raises:

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

### get_slot

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get_slot(
var,
slot_name
)


### get_slot_names

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get_slot_names()


A list of names for this optimizer's slots.

### get_updates

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get_updates(
loss,
params
)


### get_weights

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get_weights()


### minimize

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minimize(
loss,
var_list,
name=None
)


Minimize loss by updating var_list.

This method simply computes gradient using tf.GradientTape and calls apply_gradients(). If you want to process the gradient before applying then call tf.GradientTape and apply_gradients() explicitly instead of using this function.

#### Args:

• loss: A callable taking no arguments which returns the value to minimize.
• var_list: list or tuple of Variable objects to update to minimize loss, or a callable returning the list or tuple of Variable objects. Use callable when the variable list would otherwise be incomplete before minimize since the variables are created at the first time loss is called.
• grad_loss: Optional. A Tensor holding the gradient computed for loss.
• name: Optional name for the returned operation.

#### Returns:

An Operation that updates the variables in var_list. If global_step was not None, that operation also increments global_step.

#### Raises:

• ValueError: If some of the variables are not Variable objects.

### set_weights

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set_weights(weights)


### variables

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variables()


Returns variables of this Optimizer based on the order created.