# tf.contrib.opt.MovingAverageOptimizer

## Class MovingAverageOptimizer

Inherits From: Optimizer

Optimizer that computes a moving average of the variables.

Empirically it has been found that using the moving average of the trained parameters of a deep network is better than using its trained parameters directly. This optimizer allows you to compute this moving average and swap the variables at save time so that any code outside of the training loop will use by default the averaged values instead of the original ones.

Example of usage:


// Encapsulate your favorite optimizer (here the momentum one)
// inside the MovingAverageOptimizer.
opt = tf.train.MomentumOptimizer(learning_rate, FLAGS.momentum)
opt = tf.contrib.opt.MovingAverageOptimizer(opt)
// Then create your model and all its variables.
model = build_model()
// Add the training op that optimizes using opt.
// This needs to be called before swapping_saver().
opt.minimize(cost, var_list)
// Then create your saver like this:
saver = opt.swapping_saver()
// Pass it to your training loop.
slim.learning.train(
model,
...
saver=saver)


Note that for evaluation, the normal saver should be used instead of swapping_saver().

## Methods

### __init__

__init__(
opt,
average_decay=0.9999,
sequential_update=True
)


Construct a new MovingAverageOptimizer.

#### Args:

• opt: A tf.Optimizer that will be used to compute and apply gradients.
• average_decay: Float. Decay to use to maintain the moving averages of trained variables. See tf.train.ExponentialMovingAverage for details.
• num_updates: Optional count of number of updates applied to variables. See tf.train.ExponentialMovingAverage for details.
• sequential_update: Bool. If False, will compute the moving average at the same time as the model is updated, potentially doing benign data races. If True, will update the moving average after gradient updates.

### apply_gradients

apply_gradients(
global_step=None,
name=None
)


### compute_gradients

compute_gradients(
*args,
**kwargs
)


### 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.

### 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.

### swapping_saver

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


Create a saver swapping moving averages and variables.

You should use this saver during training. It will save the moving averages of the trained parameters under the original parameter names. For evaluations or inference you should use a regular saver and it will automatically use the moving averages for the trained variable.

You must call this function after all variables have been created and after you have called Optimizer.minimize().

#### Args:

• var_list: List of variables to save, as per Saver(). If set to None, will save all the variables that have been created before this call.
• name: The name of the saver.
• **kwargs: Keyword arguments of Saver().

#### Returns:

A tf.train.Saver object.

#### Raises:

• RuntimeError: If apply_gradients or minimize has not been called before.
• ValueError: If var_list is provided and contains some variables but not their moving average counterpart.

### 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.