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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:


Class Variables

  • ACTIVATIONS = 'activations'
  • ASSET_FILEPATHS = 'asset_filepaths'
  • BIASES = 'biases'
  • CONCATENATED_VARIABLES = 'concatenated_variables'
  • COND_CONTEXT = 'cond_context'
  • EVAL_STEP = 'eval_step'
  • GLOBAL_STEP = 'global_step'
  • GLOBAL_VARIABLES = 'variables'
  • INIT_OP = 'init_op'
  • LOCAL_INIT_OP = 'local_init_op'
  • LOCAL_RESOURCES = 'local_resources'
  • LOCAL_VARIABLES = 'local_variables'
  • LOSSES = 'losses'
  • METRIC_VARIABLES = 'metric_variables'
  • MODEL_VARIABLES = 'model_variables'
  • MOVING_AVERAGE_VARIABLES = 'moving_average_variables'
  • QUEUE_RUNNERS = 'queue_runners'
  • READY_FOR_LOCAL_INIT_OP = 'ready_for_local_init_op'
  • READY_OP = 'ready_op'
  • REGULARIZATION_LOSSES = 'regularization_losses'
  • RESOURCES = 'resources'
  • SAVEABLE_OBJECTS = 'saveable_objects'
  • SAVERS = 'savers'
  • SUMMARIES = 'summaries'
  • SUMMARY_OP = 'summary_op'
  • TABLE_INITIALIZERS = 'table_initializer'
  • TRAINABLE_RESOURCE_VARIABLES = 'trainable_resource_variables'
  • TRAINABLE_VARIABLES = 'trainable_variables'
  • TRAIN_OP = 'train_op'
  • UPDATE_OPS = 'update_ops'
  • VARIABLES = 'variables'
  • WEIGHTS = 'weights'
  • WHILE_CONTEXT = 'while_context'