Merge changes from github.

Change: 153426348

2 files changed, 5 insertions, 2 deletions

diff --git a/tensorflow/examples/learn/iris_custom_decay_dnn.py b/tensorflow/examples/learn/iris_custom_decay_dnn.py
index 73c526cd4e..31acbd30cd 100644
--- a/tensorflow/examples/learn/iris_custom_decay_dnn.py
+++ b/tensorflow/examples/learn/iris_custom_decay_dnn.py
@@ -11,6 +11,8 @@
 #  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
+"""Example of DNNClassifier for Iris plant dataset, with exponential decay."""
+
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
diff --git a/tensorflow/examples/learn/text_classification_character_cnn.py b/tensorflow/examples/learn/text_classification_character_cnn.py
index 0c96976146..5ad53acf9f 100644
--- a/tensorflow/examples/learn/text_classification_character_cnn.py
+++ b/tensorflow/examples/learn/text_classification_character_cnn.py
@@ -11,7 +11,8 @@
 #  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
-"""This is an example of using convolutional networks over characters for DBpedia dataset to predict class from description of an entity.
+"""This is an example of using convolutional networks over characters for
+   DBpedia dataset to predict class from description of an entity.
 
 This model is similar to one described in this paper:
    "Character-level Convolutional Networks for Text Classification"
@@ -54,7 +55,7 @@ def char_cnn_model(features, target):
     # Apply Convolution filtering on input sequence.
     conv1 = tf.contrib.layers.convolution2d(
         byte_list, N_FILTERS, FILTER_SHAPE1, padding='VALID')
-    # Add a RELU for non linearity.
+    # Add a ReLU for non linearity.
     conv1 = tf.nn.relu(conv1)
     # Max pooling across output of Convolution+Relu.
     pool1 = tf.nn.max_pool(