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# Variables
Note: Functions taking `Tensor` arguments can also take anything accepted by
@{tf.convert_to_tensor}.
[TOC]
## 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_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}
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