tf.compat.v1.GraphDef

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

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'`

TensorFlow

tf.compat / v1 / v1.GraphDef

Class Variables

TensorFlow

tf

tf.audio

tf.autograph

tf.bitwise

tf.compat

tf.config

tf.data

tf.debugging

tf.distribute

tf.dtypes

tf.errors

tf.estimator

tf.experimental

tf.feature_column

tf.graph_util

tf.image

tf.initializers

tf.io

tf.keras

tf.linalg

tf.lite

tf.lookup

tf.losses

tf.math

tf.metrics

tf.nest

tf.nn

tf.optimizers

tf.quantization

tf.queue

tf.ragged

tf.random

tf.raw_ops

tf.saved_model

tf.sets

tf.signal

tf.sparse

tf.strings

tf.summary

tf.sysconfig

tf.test

tf.tpu

tf.train

tf.version

tf.xla

tf.compat / v1 / v1.GraphDef

Class Variables