tf.contrib.opt.ElasticAverageOptimizer

Wrapper optimizer that implements the Elastic Average SGD algorithm.

Inherits From: Optimizer

tf.contrib.opt.ElasticAverageOptimizer(
    opt, num_worker, ea_custom_getter, communication_period=10, moving_rate=None,
    rho=None, use_locking=True, synchronous=False, name='ElasticAverageOptimizer'
)

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.

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 in the model. The default value is moving_rate/learning_rate rho=0.0 is suggested in async mode.
use_locking	If True use locks for update operations.
synchronous	Add_sync_queues_and_barrier or not. True: all workers will wait for each other before start training False: worker can start training when its initilization is done, no need to wait for everyone is ready. in case one worker is restarted, it can join and continue training without being blocked.
name	Optional name prefix for the operations created when applying gradients. Defaults to "ElasticAverageOptimizer".

Methods

apply_gradients

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apply_gradients(
    grads_and_vars, global_step=None, name=None
)

Apply gradients to global variables.

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

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compute_gradients(
    loss, var_list=None, gate_gradients=optimizer.Optimizer.GATE_OP,
    aggregation_method=None, colocate_gradients_with_ops=False, grad_loss=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

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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

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get_name()

get_slot

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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

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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

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make_session_run_hook(
    is_chief, task_index
)

Creates a hook to handle ElasticAverageOptimizerHook ops such as initialization.

minimize

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minimize(
    loss, global_step=None, var_list=None, gate_gradients=GATE_OP,
    aggregation_method=None, colocate_gradients_with_ops=False, name=None,
    grad_loss=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 no arguments and computes the value to be minimized. 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.

swapping_saver

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swapping_saver(
    var_list=None, name='swapping_saver', **kwargs
)

Create a saver copy global_center_variable to trainable variables

Please call this function after all your variables created with ElasticAverageCustomGetter. For evaluations or inference, use this saver during training. It will save the global_center_variable of the trained parameters under the original parameter names. Args: var_list: List of variables to save, as per Saver(). If set to None, save all the trainable_variables that have been created before this call. name: The name of the saver. **kwargs: Keyword arguments of Saver().

Returns
A tf.compat.v1.train.Saver object.

Raises
RuntimeError	global_center_variable is empty, please make sure this is called after model created and ElasticAverageCustomGetter is used when declaring you model

variables

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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.

Class Variables

• BETA = 0.9
• GATE_GRAPH = 2
• GATE_NONE = 0
• GATE_OP = 1