Update generated Python Op docs.

Change: 147423074

5 files changed, 5 insertions, 96 deletions

diff --git a/tensorflow/g3doc/api_docs/python/control_flow_ops.md b/tensorflow/g3doc/api_docs/python/control_flow_ops.md
index cfcec97c91..cffc790d60 100644
--- a/tensorflow/g3doc/api_docs/python/control_flow_ops.md
+++ b/tensorflow/g3doc/api_docs/python/control_flow_ops.md
@@ -7,10 +7,7 @@ Note: Functions taking `Tensor` arguments can also take anything accepted by
 
 [TOC]
 
-## Control Flow Operations
-
-TensorFlow provides several operations and classes that you can use to control
-the execution of operations and add conditional dependencies to your graph.
+Control Flow Operations. See the @{python/control_flow_ops} guide.
 
 - - -
 
@@ -390,12 +387,6 @@ Example using shape_invariants:
   ```
 
 
-
-## Logical Operators
-
-TensorFlow provides several operations that you can use to add logical operators
-to your graph.
-
 - - -
 
 ### `tf.logical_and(x, y, name=None)` {#logical_and}
@@ -462,12 +453,6 @@ Returns the truth value of x OR y element-wise.
 x ^ y = (x | y) & ~(x & y).
 
 
-
-## Comparison Operators
-
-TensorFlow provides several operations that you can use to add comparison
-operators to your graph.
-
 - - -
 
 ### `tf.equal(x, y, name=None)` {#equal}
@@ -644,12 +629,6 @@ has the same shape as `x` and `y`, then it chooses which element to copy from
 *  <b>`ValueError`</b>: When exactly one of `x` or `y` is non-None.
 
 
-
-## Debugging Operations
-
-TensorFlow provides several operations that you can use to validate values and
-debug your graph.
-
 - - -
 
 ### `tf.is_finite(x, name=None)` {#is_finite}
diff --git a/tensorflow/g3doc/api_docs/python/script_ops.md b/tensorflow/g3doc/api_docs/python/script_ops.md
index e7ee7365f3..13e9feb865 100644
--- a/tensorflow/g3doc/api_docs/python/script_ops.md
+++ b/tensorflow/g3doc/api_docs/python/script_ops.md
@@ -7,10 +7,7 @@ Note: Functions taking `Tensor` arguments can also take anything accepted by
 
 [TOC]
 
-## Script Language Operators.
-
-TensorFlow provides allows you to wrap python/numpy functions as
-TensorFlow operators.
+Script Language Operators. See the @{python/script_ops} guide.
 
 - - -
 
diff --git a/tensorflow/g3doc/api_docs/python/session_ops.md b/tensorflow/g3doc/api_docs/python/session_ops.md
index 342caaf715..9794923c79 100644
--- a/tensorflow/g3doc/api_docs/python/session_ops.md
+++ b/tensorflow/g3doc/api_docs/python/session_ops.md
@@ -7,10 +7,7 @@ Note: Functions taking `Tensor` arguments can also take anything accepted by
 
 [TOC]
 
-## Tensor Handle Operations.
-
-TensorFlow provides several operators that allows the user to keep tensors
-"in-place" across run calls.
+Tensor Handle Operations. See the @{python/session_ops} guide.
 
 - - -
 
diff --git a/tensorflow/g3doc/api_docs/python/sparse_ops.md b/tensorflow/g3doc/api_docs/python/sparse_ops.md
index 47771a24fa..b933d2251b 100644
--- a/tensorflow/g3doc/api_docs/python/sparse_ops.md
+++ b/tensorflow/g3doc/api_docs/python/sparse_ops.md
@@ -7,12 +7,7 @@ Note: Functions taking `Tensor` arguments can also take anything accepted by
 
 [TOC]
 
-## Sparse Tensor Representation
-
-TensorFlow supports a `SparseTensor` representation for data that is sparse
-in multiple dimensions. Contrast this representation with `IndexedSlices`,
-which is efficient for representing tensors that are sparse in their first
-dimension, and dense along all other dimensions.
+Sparse Tensor Representation. See the @{python/sparse_ops} guide.
 
 - - -
 
@@ -322,9 +317,6 @@ Alias for field number 1
 
 
 
-
-## Conversion
-
 - - -
 
 ### `tf.sparse_to_dense(sparse_indices, output_shape, sparse_values, default_value=0, validate_indices=True, name=None)` {#sparse_to_dense}
@@ -575,9 +567,6 @@ In this case the resulting `SparseTensor` has the following properties:
 *  <b>`ValueError`</b>: If `sp_ids` and `vocab_size` are lists of different lengths.
 
 
-
-## Manipulation
-
 - - -
 
 ### `tf.sparse_concat(axis, sp_inputs, name=None, expand_nonconcat_dim=False, concat_dim=None)` {#sparse_concat}
@@ -1026,8 +1015,6 @@ then the output will be a `SparseTensor` of shape `[5, 4]` and
 *  <b>`TypeError`</b>: If `sp_input` is not a `SparseTensor`.
 
 
-
-## Reduction
 - - -
 
 ### `tf.sparse_reduce_sum(sp_input, axis=None, keep_dims=False, reduction_axes=None)` {#sparse_reduce_sum}
@@ -1107,8 +1094,6 @@ which are interpreted according to the indexing rules in Python.
   The reduced SparseTensor.
 
 
-
-## Math Operations
 - - -
 
 ### `tf.sparse_add(a, b, thresh=0)` {#sparse_add}
diff --git a/tensorflow/g3doc/api_docs/python/state_ops.md b/tensorflow/g3doc/api_docs/python/state_ops.md
index cc3cdd33a5..5477beda8a 100644
--- a/tensorflow/g3doc/api_docs/python/state_ops.md
+++ b/tensorflow/g3doc/api_docs/python/state_ops.md
@@ -7,7 +7,7 @@ Note: Functions taking `Tensor` arguments can also take anything accepted by
 
 [TOC]
 
-## Variables
+Variables. See the @{python/state_ops} guide.
 
 - - -
 
@@ -1170,12 +1170,6 @@ is on a different device it will get a copy of the variable.
 
 
 
-
-## Variable helper functions
-
-TensorFlow provides a set of functions to help manage the set of variables
-collected in the graph.
-
 - - -
 
 ### `tf.global_variables()` {#global_variables}
@@ -1261,7 +1255,6 @@ This convenience function returns the contents of that collection.
   A list of Variable objects.
 
 
-
 - - -
 
 ### `tf.global_variables_initializer()` {#global_variables_initializer}
@@ -1381,7 +1374,6 @@ logged by the C++ runtime. This is expected.
   An Op, or None if there are no variables.
 
 
-
 - - -
 
 ### `tf.assign(ref, value, validate_shape=None, use_locking=None, name=None)` {#assign}
@@ -1471,9 +1463,6 @@ This makes it easier to chain operations that need to use the reset value.
   to use the new value after the variable has been updated.
 
 
-
-## Saving and Restoring Variables
-
 - - -
 
 ### `class tf.train.Saver` {#Saver}
@@ -1851,7 +1840,6 @@ Converts this `Saver` to a `SaverDef` protocol buffer.
 
 
 
-
 - - -
 
 ### `tf.train.latest_checkpoint(checkpoint_dir, latest_filename=None)` {#latest_checkpoint}
@@ -1871,7 +1859,6 @@ Finds the filename of latest saved checkpoint file.
   The full path to the latest checkpoint or `None` if no checkpoint was found.
 
 
-
 - - -
 
 ### `tf.train.get_checkpoint_state(checkpoint_dir, latest_filename=None)` {#get_checkpoint_state}
@@ -1926,12 +1913,6 @@ proto.
 *  <b>`RuntimeError`</b>: If the save paths conflict.
 
 
-
-## Sharing Variables
-
-TensorFlow provides several classes and operations that you can use to
-create variables contingent on certain conditions.
-
 - - -
 
 ### `tf.get_variable(name, shape=None, dtype=None, initializer=None, regularizer=None, trainable=True, collections=None, caching_device=None, partitioner=None, validate_shape=True, use_resource=None, custom_getter=None)` {#get_variable}
@@ -2506,7 +2487,6 @@ reduce the likelihood of collisions with kwargs.
 *  <b>`ValueError`</b>: if the name is None.
 
 
-
 - - -
 
 ### `tf.no_regularizer(_)` {#no_regularizer}
@@ -2514,7 +2494,6 @@ reduce the likelihood of collisions with kwargs.
 Use this function to prevent regularization of variables.
 
 
-
 - - -
 
 ### `class tf.constant_initializer` {#constant_initializer}
@@ -2820,9 +2799,6 @@ Args:
 
 
 
-
-## Variable Partitioners for Sharding
-
 - - -
 
 ### `tf.fixed_size_partitioner(num_shards, axis=0)` {#fixed_size_partitioner}
@@ -2908,21 +2884,6 @@ variable. The maximum number of such partitions (upper bound) is given by
   `variable_scope`, `get_variable`, and `get_partitioned_variable_list`.
 
 
-
-## 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`](../../api_docs/python/array_ops.md#gather)),
-an [`IndexedSlices`](#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(ref, indices, updates, use_locking=None, name=None)` {#scatter_update}
@@ -3499,9 +3460,6 @@ A `Tensor` containing the values of the slices.
 
 
 
-
-### Read-only Lookup Tables
-
 - - -
 
 ### `tf.initialize_all_tables(*args, **kwargs)` {#initialize_all_tables}
@@ -3540,10 +3498,6 @@ Returns an Op that initializes all tables of the default graph.
   not tables the returned Op is a NoOp.
 
 
-
-
-## Exporting and Importing Meta Graphs
-
 - - -
 
 ### `tf.train.export_meta_graph(filename=None, meta_info_def=None, graph_def=None, saver_def=None, collection_list=None, as_text=False, graph=None, export_scope=None, clear_devices=False, **kwargs)` {#export_meta_graph}
@@ -3657,9 +3611,6 @@ device assignments have not changed.
   (i.e., there are no variables to restore).
 
 
-
-# Deprecated functions (removed after 2017-03-02). Please don't use them.
-
 - - -
 
 ### `tf.all_variables(*args, **kwargs)` {#all_variables}