# tf.GraphKeys

## Class GraphKeys

Defined in tensorflow/python/framework/ops.py.

See the guide: Building Graphs > Graph collections

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.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.trainable_variables for more details.
• SUMMARIES: the summary Tensor objects that have been created in the graph. See tf.summary.merge_all for more details.
• QUEUE_RUNNERS: the QueueRunner objects that are used to produce input for a computation. See tf.train.start_queue_runners for more details.
• MOVING_AVERAGE_VARIABLES: the subset of Variable objects that will also keep moving averages. See tf.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:

• WEIGHTS
• BIASES
• ACTIVATIONS