tf.contrib.opt.MovingAverageOptimizer

class tf.contrib.opt.MovingAverageOptimizer

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__(opt, average_decay=0.9999, num_updates=None, 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(grads_and_vars, global_step=None, name=None)

compute_gradients(loss, var_list=None, gate_gradients=GATE_OP, aggregation_method=None, colocate_gradients_with_ops=False, grad_loss=None)

Compute gradients of loss for the variables in var_list.

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 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_name()

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()

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

See get_slot().

Returns:

A list of strings.

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 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.

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.Saver object.

Raises:

• RuntimeError: If apply_gradients or minimize has not been called before.

Class Members

GATE_GRAPH

GATE_NONE

GATE_OP

Defined in tensorflow/contrib/opt/python/training/moving_average_optimizer.py.