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Optimizer that implements the Adamax algorithm.

Inherits From: Optimizer

### Aliases:

It is a variant of Adam based on the infinity norm. Default parameters follow those provided in the paper. Adamax is sometimes superior to adam, specially in models with embeddings.

References see Section 7 of Kingma et al., 2014 (pdf).

## __init__

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__init__(
learning_rate=0.001,
beta_1=0.9,
beta_2=0.999,
epsilon=1e-07,
**kwargs
)

#### Initialization:

m_0 <- 0 (Initialize initial 1st moment vector)
v_0 <- 0 (Initialize the exponentially weighted infinity norm)
t <- 0 (Initialize timestep)

The update rule for variable with gradient g uses an optimization described at the end of section 7.1 of the paper:

t <- t + 1

m_t <- beta1 * m_{t-1} + (1 - beta1) * g
v_t <- max(beta2 * v_{t-1}, abs(g))
variable <- variable - learning_rate / (1 - beta1^t) * m_t / (v_t + epsilon)

Similar to AdamOptimizer, the epsilon is added for numerical stability (especially to get rid of division by zero when v_t = 0).

Contrast to AdamOptimizer, the sparse implementation of this algorithm (used when the gradient is an IndexedSlices object, typically because of tf.gather or an embedding lookup in the forward pass) only updates variable slices and corresponding m_t, v_t terms when that part of the variable was used in the forward pass. This means that the sparse behavior is contrast to the dense behavior (similar to some momentum implementations which ignore momentum unless a variable slice was actually used).

#### Args:

• learning_rate: A Tensor or a floating point value. The learning rate.
• beta_1: A float value or a constant float tensor. The exponential decay rate for the 1st moment estimates.
• beta_2: A float value or a constant float tensor. The exponential decay rate for the exponentially weighted infinity norm.
• epsilon: A small constant for numerical stability.
• name: Optional name for the operations created when applying gradients. Defaults to "Adamax".
• **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.

## Properties

### 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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var,
slot_name,
initializer='zeros'
)

Add a new slot variable for var.

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

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name=None
)

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. 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.

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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).

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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.

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loss,
params
)

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get_weights()

### minimize

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minimize(
loss,
var_list,
name=None
)

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.

### 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.