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tf.contrib.opt.MovingAverageOptimizer

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Optimizer that computes a moving average of the variables.

Inherits From: Optimizer

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.compat.v1.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().

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.

Methods

apply_gradients

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Apply gradients to 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.
RuntimeError If you should use _distributed_apply() instead.

compute_gradients

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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 or a callable taking no arguments which returns the value to minimize. When eager execution is enabled it must be a callable.
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 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.
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.
RuntimeError If called with eager execution enabled and loss is not callable.

Eager Compatibility

When eager execution is enabled, gate_gradients, aggregation_method, and colocate_gradients_with_ops are ignored.

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.

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.

swapping_saver

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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.compat.v1.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

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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