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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 ofVariableobjects, shared across distributed environment (model variables are subset of these). Seetf.compat.v1.global_variablesfor more details. Commonly, allTRAINABLE_VARIABLESvariables will be inMODEL_VARIABLES, and allMODEL_VARIABLESvariables will be inGLOBAL_VARIABLES.LOCAL_VARIABLES: the subset ofVariableobjects that are local to each machine. Usually used for temporarily variables, like counters.MODEL_VARIABLES: the subset ofVariableobjects that are used in the model for inference (feed forward).TRAINABLE_VARIABLES: the subset ofVariableobjects that will be trained by an optimizer. Seetf.compat.v1.trainable_variablesfor more details.SUMMARIES: the summaryTensorobjects that have been created in the graph. Seetf.compat.v1.summary.merge_allfor more details.QUEUE_RUNNERS: theQueueRunnerobjects that are used to produce input for a computation. Seetf.compat.v1.train.start_queue_runnersfor more details.MOVING_AVERAGE_VARIABLES: the subset ofVariableobjects that will also keep moving averages. Seetf.compat.v1.moving_average_variablesfor 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
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