# tf.contrib.opt.ElasticAverageOptimizer

## Class ElasticAverageOptimizer

Inherits From: Optimizer

Wrapper optimizer that implements the Elastic Average SGD algorithm. This is an async optimizer. During the training, Each worker will update the local variables and maintains its own local_step, which starts from 0 and is incremented by 1 after each update of local variables. Whenever the communication period divides the local step, the worker requests the current global center variables and then computed the elastic difference between global center variables and local variables. The elastic difference then be used to update both local variables and global variables.

## Methods

### __init__

__init__(
opt,
num_worker,
ea_custom_getter,
communication_period=10,
moving_rate=None,
rho=None,
use_locking=True,
name='ElasticAverageOptimizer'
)


Construct a new gradient descent optimizer.

#### Args:

• opt: The actual optimizer that will be used to update local variables. Must be one of the Optimizer classes.
• num_worker: The number of workers
• ea_custom_getter: The ElasticAverageCustomGetter
• communication_period: An int point value to controls the frequency of the communication between every worker and the ps.
• moving_rate: A floating point value to control the elastic difference.
• rho: the amount of exploration we allow ine the model. The default value is moving_rate/learning_rate
• use_locking: If True use locks for update operations.
• name: Optional name prefix for the operations created when applying gradients. Defaults to "ElasticAverageOptimizer".

### apply_gradients

apply_gradients(
global_step=None,
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 as returned by compute_gradients().
• global_step: Optional Variable to increment by one after the variables have been updated.
• 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.

### compute_gradients

compute_gradients(
loss,
var_list=None,
aggregation_method=None,
)


Compute gradients of loss for the variables in var_list.

Add rho*elastic_difference to loss to control the exploration This is the first part of minimize(). It returns a list of (gradient, variable) pairs where "gradient" is the gradient for "variable". Note that "gradient" can be a Tensor, an IndexedSlices, or None if there is no gradient for the given variable.

#### Args:

• loss: A Tensor containing the value to minimize.
• var_list: Optional list or tuple of tf.Variable to update to minimize loss. Defaults to the list of variables collected in the graph under the key GraphKey.TRAINABLE_VARIABLES.
• gate_gradients: How to gate the computation of gradients. Can be GATE_NONE, GATE_OP, or GATE_GRAPH.
• aggregation_method: Specifies the method used to combine gradient terms. Valid values are defined in the class AggregationMethod.
• colocate_gradients_with_ops: If True, try colocating gradients with the corresponding op.
• grad_loss: Optional. A Tensor holding the gradient computed for loss.

#### Returns:

A list of (gradient, variable) pairs. Variable is always present, but gradient can be None.

#### Raises:

• TypeError: If var_list contains anything else than Variable objects.
• ValueError: If some arguments are invalid.

### get_init_op

get_init_op(task_index)


Returns the op to let all the local variables and local center variables equal to the global center variables before the training begins

### get_name

get_name()


### get_slot

get_slot(
var,
name
)


Return a slot named name created for var by the Optimizer.

Some Optimizer subclasses use additional variables. For example Momentum and Adagrad use variables to accumulate updates. This method gives access to these Variable objects if for some reason you need them.

Use get_slot_names() to get the list of slot names created by the Optimizer.

#### Args:

• var: A variable passed to minimize() or apply_gradients().
• name: A string.

#### Returns:

The Variable for the slot if it was created, None otherwise.

### get_slot_names

get_slot_names()


Return a list of the names of slots created by the Optimizer.

See get_slot().

#### Returns:

A list of strings.

### make_session_run_hook

make_session_run_hook(
is_chief,
)


Creates a hook to handle ElasticAverageOptimizerHook ops such as initialization.

### minimize

minimize(
loss,
global_step=None,
var_list=None,
aggregation_method=None,
name=None,
)


Add operations to minimize loss by updating var_list.

This method simply combines calls compute_gradients() and apply_gradients(). If you want to process the gradient before applying them call compute_gradients() and apply_gradients() explicitly instead of using this function.

#### Args:

• loss: A Tensor containing the value to minimize.
• global_step: Optional Variable to increment by one after the variables have been updated.
• var_list: Optional list or tuple of Variable objects to update to minimize loss. Defaults to the list of variables collected in the graph under the key GraphKeys.TRAINABLE_VARIABLES.
• gate_gradients: How to gate the computation of gradients. Can be GATE_NONE, GATE_OP, or GATE_GRAPH.
• aggregation_method: Specifies the method used to combine gradient terms. Valid values are defined in the class AggregationMethod.
• colocate_gradients_with_ops: If True, try colocating gradients with the corresponding op.
• name: Optional name for the returned operation.
• grad_loss: Optional. A Tensor holding the gradient computed for loss.

#### 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.

#### Eager Compatibility

When eager execution is enabled, loss should be a Python function that takes elements of var_list as arguments and computes the value to be minimized. If var_list is None, loss should take no arguments. Minimization (and gradient computation) is done with respect to the elements of var_list if not None, else with respect to any trainable variables created during the execution of the loss function. gate_gradients, aggregation_method, colocate_gradients_with_ops and grad_loss are ignored when eager execution is enabled.

### variables

variables()


A list of variables which encode the current state of Optimizer.

Includes slot variables and additional global variables created by the optimizer in the current default graph.

#### Returns:

A list of variables.