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description: Standard names to use for graph collections.
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View source on GitHub
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Standard names to use for graph collections.
The standard library uses various well-known names to collect and
retrieve values associated with a graph. For example, the
tf.Optimizer subclasses default to optimizing the variables
collected under tf.GraphKeys.TRAINABLE_VARIABLES if none is
specified, but it is also possible to pass an explicit list of
variables.
The following standard keys are defined:
GLOBAL_VARIABLES: the default collection of Variable objects, shared
across distributed environment (model variables are subset of these). See
tf.compat.v1.global_variables
for more details.
Commonly, all TRAINABLE_VARIABLES variables will be in MODEL_VARIABLES,
and all MODEL_VARIABLES variables will be in GLOBAL_VARIABLES.LOCAL_VARIABLES: the subset of Variable objects that are local to each
machine. Usually used for temporarily variables, like counters.
Note: use tf.contrib.framework.local_variable to add to this collection.MODEL_VARIABLES: the subset of Variable objects that are used in the
model for inference (feed forward). Note: use
tf.contrib.framework.model_variable to add to this collection.TRAINABLE_VARIABLES: the subset of Variable objects that will
be trained by an optimizer. See
tf.compat.v1.trainable_variables
for more details.SUMMARIES: the summary Tensor objects that have been created in the
graph. See
tf.compat.v1.summary.merge_all
for more details.QUEUE_RUNNERS: the QueueRunner objects that are used to
produce input for a computation. See
tf.compat.v1.train.start_queue_runners
for more details.MOVING_AVERAGE_VARIABLES: the subset of Variable objects that will also
keep moving averages. See
tf.compat.v1.moving_average_variables
for more details.REGULARIZATION_LOSSES: regularization losses collected during graph
construction.The following standard keys are defined, but their collections are not automatically populated as many of the others are:
WEIGHTSBIASESACTIVATIONS