Variables
[TOC]
Variables
Variable helper functions
TensorFlow provides a set of functions to help manage the set of variables
collected in the graph.
tf.global_variablestf.local_variablestf.model_variablestf.trainable_variablestf.moving_average_variablestf.global_variables_initializertf.local_variables_initializertf.variables_initializertf.is_variable_initializedtf.report_uninitialized_variablestf.assert_variables_initializedtf.assigntf.assign_addtf.assign_sub
Saving and Restoring Variables
tf.train.Savertf.train.latest_checkpointtf.train.get_checkpoint_statetf.train.update_checkpoint_state
Sharing Variables
TensorFlow provides several classes and operations that you can use to
create variables contingent on certain conditions.
tf.get_variabletf.get_local_variabletf.VariableScopetf.variable_scopetf.variable_op_scopetf.get_variable_scopetf.make_templatetf.no_regularizertf.constant_initializertf.random_normal_initializertf.truncated_normal_initializertf.random_uniform_initializertf.uniform_unit_scaling_initializertf.zeros_initializertf.ones_initializertf.orthogonal_initializer
Variable Partitioners for Sharding
Sparse Variable Updates
The sparse update ops modify a subset of the entries in a dense Variable,
either overwriting the entries or adding / subtracting a delta. These are
useful for training embedding models and similar lookup-based networks, since
only a small subset of embedding vectors change in any given step.
tf.scatter_updatetf.scatter_addtf.scatter_subtf.scatter_multf.scatter_divtf.scatter_mintf.scatter_maxtf.scatter_nd_updatetf.scatter_nd_addtf.scatter_nd_subtf.sparse_masktf.IndexedSlices