Inherits From: Optimizer

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:

$$E[g^2]_0 := 0 \text{(Initialize gradient 2nd order moment vector)}$$
$$E[\Delta x^2]_0 := 0 \text{(Initialize 2nd order variable update)}$$
$$t := t + 1$$
$$E[g^2]_t := \rho * E[g^2]_{t-1} + (1 - \rho) * g^2$$
$$\Delta x_t = -RMS[\Delta x]_{t-1} * g_t / RMS[g]_t$$
$$E[\Delta x^2]_t := \rho * E[\Delta x^2]_{t-1} + (1 - \rho) * \Delta x_t^2$$
$$x_t := x_{t-1} + \Delta x_{t}$$

References See M. D. Zeiler (pdf)

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.
**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.

iterations Variable. The number of training steps this Optimizer has run.
weights Returns variables of this Optimizer based on the order created.

## Methods

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Add a new slot variable for var.

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

Args
name Optional name for the returned operation. Default to the name passed to the Optimizer constructor.

Returns
An Operation that applies the specified gradients. The iterations will be automatically increased by 1.

Raises
ValueError If none of the variables have gradients.

### from_config

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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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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.

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Returns gradients of loss with respect to params.

Arguments
loss Loss tensor.
params List of variables.

Returns

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

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### get_slot_names

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A list of names for this optimizer's slots.

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### minimize

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Minimize loss by updating var_list.

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.
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.

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### variables

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Returns variables of this Optimizer based on the order created.

[{ "type": "thumb-down", "id": "missingTheInformationINeed", "label":"Não contém as informações de que eu preciso" },{ "type": "thumb-down", "id": "tooComplicatedTooManySteps", "label":"Muito complicado / etapas demais" },{ "type": "thumb-down", "id": "outOfDate", "label":"Desatualizado" },{ "type": "thumb-down", "id": "samplesCodeIssue", "label":"Samples / code issue" },{ "type": "thumb-down", "id": "otherDown", "label":"Outro" }]
[{ "type": "thumb-up", "id": "easyToUnderstand", "label":"Fácil de entender" },{ "type": "thumb-up", "id": "solvedMyProblem", "label":"Meu problema foi resolvido" },{ "type": "thumb-up", "id": "otherUp", "label":"Outro" }]