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tf.train.ExponentialMovingAverage

Maintains moving averages of variables by employing an exponential decay.

When training a model, it is often beneficial to maintain moving averages of the trained parameters. Evaluations that use averaged parameters sometimes produce significantly better results than the final trained values.

The apply() method adds shadow copies of trained variables and add ops that maintain a moving average of the trained variables in their shadow copies. It is used when building the training model. The ops that maintain moving averages are typically run after each training step. The average() and average_name() methods give access to the shadow variables and their names. They are useful when building an evaluation model, or when restoring a model from a checkpoint file. They help use the moving averages in place of the last trained values for evaluations.

The moving averages are computed using exponential decay. You specify the decay value when creating the ExponentialMovingAverage object. The shadow variables are initialized with the same initial values as the trained variables. When you run the ops to maintain the moving averages, each shadow variable is updated with the formula:

shadow_variable -= (1 - decay) * (shadow_variable - variable)

This is mathematically equivalent to the classic formula below, but the use of an assign_sub op (the "-=" in the formula) allows concurrent lockless updates to the variables:

shadow_variable = decay * shadow_variable + (1 - decay) * variable

Reasonable values for decay are close to 1.0, typically in the multiple-nines range: 0.999, 0.9999, etc.

Example usage when creating a training model:

# Create variables.
var0 = tf.Variable(...)
var1 = tf.Variable(...)
# ... use the variables to build a training model...
...
# Create an op that applies the optimizer.  This is what we usually
# would use as a training op.
opt_op = opt.minimize(my_loss, [var0, var1])

# Create an ExponentialMovingAverage object
ema = tf.train.ExponentialMovingAverage(decay=0.9999)

with tf.control_dependencies([opt_op]):
    # Create the shadow variables, and add ops to maintain moving averages
    # of var0 and var1. This also creates an op that will update the moving
    # averages after each training step.  This is what we will use in place
    # of the usual training op.
    training_op = ema.apply([var0, var1])

...train the model by running training_op...

There are two ways to use the moving averages for evaluations:

  • Build a model that uses the shadow variables instead of the variables. For this, use the average() method which returns the shadow variable for a given variable.
  • Build a model normally but load the checkpoint files to evaluate by using the shadow variable names. For this use the average_name() method. See the tf.compat.v1.train.Saver for more information on restoring saved variables.

Example of restoring the shadow variable values:

# Create a Saver that loads variables from their saved shadow values.
shadow_var0_name = ema.average_name(var0)
shadow_var1_name = ema.average_name(var1)
saver = tf.compat.v1.train.Saver({shadow_var0_name: var0, shadow_var1_name:
var1})
saver.restore(...checkpoint filename...)
# var0 and var1 now hold the moving average values

decay Float. The decay to use.
num_updates Optional count of number of updates applied to variables.
zero_debias If True, zero debias moving-averages that are initialized with tensors.
name String. Optional prefix name to use for the name of ops added in apply().

name The name of this ExponentialMovingAverage object.

Methods

apply

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Maintains moving