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:


ACTIVATIONS 'activations'
ASSET_FILEPATHS 'asset_filepaths'
BIASES 'biases'
CONCATENATED_VARIABLES 'concatenated_variables'
COND_CONTEXT 'cond_context'
EVAL_STEP 'eval_step'
GLOBAL_STEP 'global_step'
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'