Variables

Note: Functions taking Tensor arguments can also take anything accepted by tf.convert_to_tensor.

Variables

• tf.Variable

Variable helper functions

TensorFlow provides a set of functions to help manage the set of variables collected in the graph.

• tf.global_variables
• tf.local_variables
• tf.model_variables
• tf.trainable_variables
• tf.moving_average_variables
• tf.global_variables_initializer
• tf.local_variables_initializer
• tf.variables_initializer
• tf.is_variable_initialized
• tf.report_uninitialized_variables
• tf.assert_variables_initialized
• tf.assign
• tf.assign_add
• tf.assign_sub

Saving and Restoring Variables

• tf.train.Saver
• tf.train.latest_checkpoint
• tf.train.get_checkpoint_state
• tf.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_variable
• tf.get_local_variable
• tf.VariableScope
• tf.variable_scope
• tf.variable_op_scope
• tf.get_variable_scope
• tf.make_template
• tf.no_regularizer
• tf.constant_initializer
• tf.random_normal_initializer
• tf.truncated_normal_initializer
• tf.random_uniform_initializer
• tf.uniform_unit_scaling_initializer
• tf.zeros_initializer
• tf.ones_initializer
• tf.orthogonal_initializer

Variable Partitioners for Sharding

• tf.fixed_size_partitioner
• tf.variable_axis_size_partitioner
• tf.min_max_variable_partitioner

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.

Since a sparse update of a large tensor may be generated automatically during gradient computation (as in the gradient of tf.gather), an tf.IndexedSlices class is provided that encapsulates a set of sparse indices and values. IndexedSlices objects are detected and handled automatically by the optimizers in most cases.

• tf.scatter_update
• tf.scatter_add
• tf.scatter_sub
• tf.scatter_mul
• tf.scatter_div
• tf.scatter_min
• tf.scatter_max
• tf.scatter_nd_update
• tf.scatter_nd_add
• tf.scatter_nd_sub
• tf.sparse_mask
• tf.IndexedSlices

Read-only Lookup Tables

• tf.initialize_all_tables
• tf.tables_initializer

Exporting and Importing Meta Graphs

• tf.train.export_meta_graph
• tf.train.import_meta_graph

Deprecated functions (removed after 2017-03-02). Please don't use them.

• tf.all_variables
• tf.initialize_all_variables
• tf.initialize_local_variables
• tf.initialize_variables