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:
WEIGHTS
BIASES
ACTIVATIONS
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'
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Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/GraphKeys