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tf.contrib.opt.ModelAverageOptimizer

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Wrapper optimizer that implements the Model Average algorithm.

Inherits From: Optimizer

This is a sync 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 interval_steps divides the local step, the local variables from all the workers will be averaged and assigned to global center variables. Then the local variables will be assigned by global center variables.

opt The actual optimizer that will be used to update local variables
num_worker The number of workers
is_chief whether chief worker
ma_custom_getter ModelAverageCustomGetter
interval_steps An int point value to controls the frequency of the average of local variables
use_locking If True use locks for update operations
name string. Optional name of the returned operation

Methods

apply_gradients

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Apply gradients to variables.

This contains most of the synchronization implementation and also wraps the apply_gradients() from the real optimizer. The chief work updates global variables.

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
A conditional 'Operation' that update both local and global variables or just local variables

Raises
ValueError If the grads_and_vars is empty.
ValueError If global step is not provided, the staleness cannot be checked.

compute_gradients

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Compute gradients of "loss" for the variables in "var_list".

This simply wraps the compute_gradients() from the real optimizer.

Args
*args Arguments for compute_gradients().
**kwargs Keyword arguments for compute_gradients().

Returns
A list of (gradient, variable) pairs.

get_init_op

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Returns the op.

This method lets all the local variables equal to the global variables before the training begins.

get_name

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get_slot

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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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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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Creates a hook to handle ModelAverage ops such as initialization.

minimize

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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.

variables

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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

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