Fix some documentation formatting errors.

Change: 150841749

2 files changed, 32 insertions, 19 deletions

diff --git a/tensorflow/contrib/linear_optimizer/python/ops/sparse_feature_column.py b/tensorflow/contrib/linear_optimizer/python/ops/sparse_feature_column.py
index ed7105b5c9..003795233f 100644
--- a/tensorflow/contrib/linear_optimizer/python/ops/sparse_feature_column.py
+++ b/tensorflow/contrib/linear_optimizer/python/ops/sparse_feature_column.py
@@ -27,28 +27,36 @@ class SparseFeatureColumn(object):
   """Represents a sparse feature column.
 
   Contains three tensors representing a sparse feature column, they are
-  example indices (int64), feature indices (int64), and feature values (float).
-  Feature weights are optional, and are treated as 1.0f if missing.
+  example indices (`int64`), feature indices (`int64`), and feature
+  values (`float`).
+  Feature weights are optional, and are treated as `1.0f` if missing.
 
   For example, consider a batch of 4 examples, which contains the following
-  features in a particular SparseFeatureColumn:
-   Example 0: feature 5, value 1
-   Example 1: feature 6, value 1 and feature 10, value 0.5
-   Example 2: no features
-   Example 3: two copies of feature 2, value 1
+  features in a particular `SparseFeatureColumn`:
+
+  * Example 0: feature 5, value 1
+  * Example 1: feature 6, value 1 and feature 10, value 0.5
+  * Example 2: no features
+  * Example 3: two copies of feature 2, value 1
 
   This SparseFeatureColumn will be represented as follows:
+
+  ```
    <0, 5,  1>
    <1, 6,  1>
    <1, 10, 0.5>
    <3, 2,  1>
    <3, 2,  1>
+  ```
 
   For a batch of 2 examples below:
-   Example 0: feature 5
-   Example 1: feature 6
 
-  is represented by SparseFeatureColumn as:
+  * Example 0: feature 5
+  * Example 1: feature 6
+
+  is represented by `SparseFeatureColumn` as:
+
+  ```
    <0, 5,  1>
    <1, 6,  1>
 
diff --git a/tensorflow/contrib/linear_optimizer/python/sdca_optimizer.py b/tensorflow/contrib/linear_optimizer/python/sdca_optimizer.py
index afa0b3b833..f9d69d6dea 100644
--- a/tensorflow/contrib/linear_optimizer/python/sdca_optimizer.py
+++ b/tensorflow/contrib/linear_optimizer/python/sdca_optimizer.py
@@ -32,6 +32,8 @@ class SDCAOptimizer(object):
   Estimator.
 
   Example usage:
+
+  ```python
     real_feature_column = real_valued_column(...)
     sparse_feature_column = sparse_column_with_hash_bucket(...)
     sdca_optimizer = linear.SDCAOptimizer(example_id_column='example_id',
@@ -44,19 +46,22 @@ class SDCAOptimizer(object):
         optimizer=sdca_optimizer)
     classifier.fit(input_fn_train, steps=50)
     classifier.evaluate(input_fn=input_fn_eval)
+  ```
 
-  Here the expectation is that the input_fn_* functions passed to train and
+  Here the expectation is that the `input_fn_*` functions passed to train and
   evaluate return a pair (dict, label_tensor) where dict has `example_id_column`
   as `key` whose value is a `Tensor` of shape [batch_size] and dtype string.
   num_loss_partitions defines the number of partitions of the global loss
-  function and should be set to (#concurrent train ops/per worker) x (#workers).
-  Convergence of (global) loss is guaranteed if num_loss_partitions is larger or
-  equal to the above product. Larger values for num_loss_partitions lead to
-  slower convergence. The recommended value for num_loss_partitions in tf.learn
-  (where currently there is one process per worker) is the number of workers
-  running the train steps. It defaults to 1 (single machine). num_table_shards
-  defines the number of shards for the internal state table, typically set to
-  match the number of parameter servers for large data sets.
+  function and should be set to `(#concurrent train ops/per worker)
+  x (#workers)`.
+  Convergence of (global) loss is guaranteed if `num_loss_partitions` is larger
+  or equal to the above product. Larger values for `num_loss_partitions` lead to
+  slower convergence. The recommended value for `num_loss_partitions` in
+  `tf.learn` (where currently there is one process per worker) is the number
+  of workers running the train steps. It defaults to 1 (single machine).
+  `num_table_shards` defines the number of shards for the internal state
+  table, typically set to match the number of parameter servers for large
+  data sets.
   """
 
   def __init__(self,