Merge changes from github.

Change: 135698415

1 files changed, 16 insertions, 18 deletions

diff --git a/tensorflow/g3doc/tutorials/deep_cnn/index.md b/tensorflow/g3doc/tutorials/deep_cnn/index.md
index d1ef5f6405..89ba53ac6f 100644
--- a/tensorflow/g3doc/tutorials/deep_cnn/index.md
+++ b/tensorflow/g3doc/tutorials/deep_cnn/index.md
@@ -32,17 +32,15 @@ new ideas and experimenting with new techniques.
 The CIFAR-10 tutorial demonstrates several important constructs for
 designing larger and more sophisticated models in TensorFlow:
 
-* Core mathematical components including [convolution](
-../../api_docs/python/nn.md#conv2d) ([wiki](
-https://en.wikipedia.org/wiki/Convolution)), [rectified linear activations](
-../../api_docs/python/nn.md#relu) ([wiki](
-https://en.wikipedia.org/wiki/Rectifier_(neural_networks))), [max pooling](
-../../api_docs/python/nn.md#max_pool) ([wiki](
-https://en.wikipedia.org/wiki/Convolutional_neural_network#Pooling_layer))
-and [local response normalization](
-../../api_docs/python/nn.md#local_response_normalization) 
-(Chapter 3.3 in [AlexNet paper](
-http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf)).
+* Core mathematical components including [convolution](../../api_docs/python/nn.md#conv2d)
+([wiki](https://en.wikipedia.org/wiki/Convolution)),
+[rectified linear activations](../../api_docs/python/nn.md#relu)
+([wiki](https://en.wikipedia.org/wiki/Rectifier_(neural_networks))),
+[max pooling](../../api_docs/python/nn.md#max_pool)
+([wiki](https://en.wikipedia.org/wiki/Convolutional_neural_network#Pooling_layer))
+and [local response normalization](../../api_docs/python/nn.md#local_response_normalization)
+(Chapter 3.3 in
+[AlexNet paper](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf)).
 * [Visualization](../../how_tos/summaries_and_tensorboard/index.md)
 of network activities during training, including input images,
 losses and distributions of activations and gradients.
@@ -57,7 +55,7 @@ that systematically decrements over time.
 for input
 data to isolate the model from disk latency and expensive image pre-processing.
 
-We also provide a [multi-GPU version](#training-a-model-using-multiple-gpu-cards) 
+We also provide a [multi-GPU version](#training-a-model-using-multiple-gpu-cards)
 of the model which demonstrates:
 
 * Configuring a model to train across multiple GPU cards in parallel.
@@ -111,7 +109,7 @@ adds operations that perform inference, i.e. classification, on supplied images.
 add operations that compute the loss,
 gradients, variable updates and visualization summaries.
 
-### Model Inputs 
+### Model Inputs
 
 The input part of the model is built by the functions `inputs()` and
 `distorted_inputs()` which read images from the CIFAR-10 binary data files.
@@ -149,7 +147,7 @@ processing time. To prevent these operations from slowing down training, we run
 them inside 16 separate threads which continuously fill a TensorFlow
 [queue](../../api_docs/python/io_ops.md#shuffle_batch).
 
-### Model Prediction 
+### Model Prediction
 
 The prediction part of the model is constructed by the `inference()` function
 which adds operations to compute the *logits* of the predictions. That part of
@@ -174,8 +172,8 @@ Here is a graph generated from TensorBoard describing the inference operation:
 </div>
 
 > **EXERCISE**: The output of `inference` are un-normalized logits. Try editing
-the network architecture to return normalized predictions using [`tf.nn.softmax()`]
-(../../api_docs/python/nn.md#softmax).
+the network architecture to return normalized predictions using
+[`tf.nn.softmax()`](../../api_docs/python/nn.md#softmax).
 
 The `inputs()` and `inference()` functions provide all the components
 necessary to perform evaluation on a model. We now shift our focus towards
@@ -188,7 +186,7 @@ layers of Alex's original model are locally connected and not fully connected.
 Try editing the architecture to exactly reproduce the locally connected
 architecture in the top layer.
 
-### Model Training 
+### Model Training
 
 The usual method for training a network to perform N-way classification is
 [multinomial logistic regression](https://en.wikipedia.org/wiki/Multinomial_logistic_regression),
@@ -307,7 +305,7 @@ values.  See how the scripts use
 [`ExponentialMovingAverage`](../../api_docs/python/train.md#ExponentialMovingAverage)
 for this purpose.
 
-## Evaluating a Model 
+## Evaluating a Model
 
 Let us now evaluate how well the trained model performs on a hold-out data set.
 The model is evaluated by the script `cifar10_eval.py`.  It constructs the model