diff options
Diffstat (limited to 'tensorflow/docs_src/api_guides/python/state_ops.md')
| -rw-r--r-- | tensorflow/docs_src/api_guides/python/state_ops.md | 110 |
1 files changed, 0 insertions, 110 deletions
diff --git a/tensorflow/docs_src/api_guides/python/state_ops.md b/tensorflow/docs_src/api_guides/python/state_ops.md deleted file mode 100644 index fc55ea1481..0000000000 --- a/tensorflow/docs_src/api_guides/python/state_ops.md +++ /dev/null @@ -1,110 +0,0 @@ -# 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_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` |