TensorFlow: Upstream latest changes to git.

Changes:
- Documentation changes: adding some examples
  for adding_an_op, fixes to some of the markdown,
  updates to docstrings, etc.

- Remove Dockerfile for now -- still undergoing
  changes.

Base CL: 107341050

1 files changed, 19 insertions, 19 deletions

diff --git a/tensorflow/g3doc/tutorials/deep_cnn/index.md b/tensorflow/g3doc/tutorials/deep_cnn/index.md
index be23e7ccaa..40d289eeef 100644
--- a/tensorflow/g3doc/tutorials/deep_cnn/index.md
+++ b/tensorflow/g3doc/tutorials/deep_cnn/index.md
@@ -1,6 +1,6 @@
 # Convolutional Neural Networks <a class="md-anchor" id="AUTOGENERATED-convolutional-neural-networks"></a>
 
-**NOTE:** This tutorial is intended for *advanced* users of TensorFlow
+> **NOTE:** This tutorial is intended for *advanced* users of TensorFlow
 and assumes expertise and experience in machine learning.
 
 ## Overview <a class="md-anchor" id="AUTOGENERATED-overview"></a>
@@ -18,28 +18,28 @@ by Alex Krizhevsky.
 ### Goals <a class="md-anchor" id="AUTOGENERATED-goals"></a>
 
 The goal of this tutorial is to build a relatively small convolutional neural
-network (CNN) for recognizing images. In the process this tutorial:
+network (CNN) for recognizing images. In the process, this tutorial:
 
 1. Highlights a canonical organization for network architecture,
 training and evaluation.
 2. Provides a template for constructing larger and more sophisticated models.
 
-The reason CIFAR-10 was selected was because it contains enough complexity to
-exercise much of TensorFlow's ability to scale to large models. At the same
-time, the model is small enough to train fast in order to test new ideas and
-experiments.
+The reason CIFAR-10 was selected was that it is complex enough to exercise
+much of TensorFlow's ability to scale to large models. At the same time,
+the model is small enough to train fast, which is ideal for trying out
+new ideas and experimenting with new techniques.
 
 ### Highlights of the Tutorial <a class="md-anchor" id="AUTOGENERATED-highlights-of-the-tutorial"></a>
 The CIFAR-10 tutorial demonstrates several important constructs for
 designing larger and more sophisticated models in TensorFlow:
 
-* Core mathematical components including[convolution](
+* Core mathematical components including [convolution](
 ../../api_docs/python/nn.md#conv2d), [rectified linear activations](
 ../../api_docs/python/nn.md#relu), [max pooling](
 ../../api_docs/python/nn.md#max_pool) and [local response normalization](
 ../../api_docs/python/nn.md#local_response_normalization).
 * [Visualization](../../how_tos/summaries_and_tensorboard/index.md)
-of network activity during training including input images,
+of network activities during training, including input images,
 losses and distributions of activations and gradients.
 * Routines for calculating the
 [moving average](../../api_docs/python/train.md#ExponentialMovingAverage)
@@ -55,7 +55,7 @@ data to isolate the model from disk latency and expensive image pre-processing.
 We also provide a multi-GPU version of the model which demonstrates:
 
 * Configuring a model to train across multiple GPU cards in parallel.
-* Sharing and updating variables between multiple GPUs.
+* Sharing and updating variables among multiple GPUs.
 
 We hope that this tutorial provides a launch point for building larger CNNs for
 vision tasks on TensorFlow.
@@ -81,10 +81,10 @@ The code for this tutorial resides in
 
 File | Purpose
 --- | ---
-[`cifar10_input.py`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/models/image/cifar10/cifar10_input.py) | Read the native CIFAR-10 binary file format.
-[`cifar10.py`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/models/image/cifar10/cifar10.py) | Build the CIFAR-10 model.
-[`cifar10_train.py`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/models/image/cifar10/cifar10_train.py) | Train a CIFAR-10 model on a single machine.
-[`cifar10_multi_gpu_train.py`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/models/image/cifar10/cifar10_multi_gpu_train.py) | Train a CIFAR-10 model on multiple GPUs.
+[`cifar10_input.py`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/models/image/cifar10/cifar10_input.py) | Reads the native CIFAR-10 binary file format.
+[`cifar10.py`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/models/image/cifar10/cifar10.py) | Builds the CIFAR-10 model.
+[`cifar10_train.py`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/models/image/cifar10/cifar10_train.py) | Trains a CIFAR-10 model on a CPU or GPU.
+[`cifar10_multi_gpu_train.py`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/models/image/cifar10/cifar10_multi_gpu_train.py) | Trains a CIFAR-10 model on multiple GPUs.
 [`cifar10_eval.py`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/models/image/cifar10/cifar10_eval.py) | Evaluates the predictive performance of a CIFAR-10 model.
 
 
@@ -130,7 +130,7 @@ artificially increase the data set size:
 
 Please see the [`Images`](../../api_docs/python/image.md) page for the list of
 available distortions. We also attach an
-[`image_summary`](../../api_docs/python/train.md?#image_summary) to the images
+[`image_summary`](../../api_docs/python/train.md#image_summary) to the images
 so that we may visualize them in TensorBoard.  This is a good practice to verify
 that inputs are built correctly.
 
@@ -169,9 +169,9 @@ Here is a graph generated from TensorBoard describing the inference operation:
 
 > **EXERCISE**: The output of `inference` are un-normalized logits. Try editing
 the network architecture to return normalized predictions using [`tf.softmax()`]
-(../../api_docs/python/nn.md?cl=head#softmax).
+(../../api_docs/python/nn.md#softmax).
 
-The `inputs()` and `inference()` functions provide all of the components
+The `inputs()` and `inference()` functions provide all the components
 necessary to perform evaluation on a model. We now shift our focus towards
 building operations for training a model.
 
@@ -196,7 +196,7 @@ For regularization, we also apply the usual
 variables.  The objective function for the model is the sum of the cross entropy
 loss and all these weight decay terms, as returned by the `loss()` function.
 
-We visualize it in TensorBoard with a [scalar_summary](../../api_docs/python/train.md?#scalar_summary):
+We visualize it in TensorBoard with a [scalar_summary](../../api_docs/python/train.md#scalar_summary):
 
 ![CIFAR-10 Loss](./cifar_loss.png "CIFAR-10 Total Loss")
 ###### [View this TensorBoard live! (Chrome/FF)](/tensorboard/cifar.html) <a class="md-anchor" id="AUTOGENERATED--view-this-tensorboard-live---chrome-ff----tensorboard-cifar.html-"></a>
@@ -214,7 +214,7 @@ over time.
 The `train()` function adds the operations needed to minimize the objective by
 calculating the gradient and updating the learned variables (see
 [`GradientDescentOptimizer`](../../api_docs/python/train.md#GradientDescentOptimizer)
-for details).  It returns an operation that executes all of the calculations
+for details).  It returns an operation that executes all the calculations
 needed to train and update the model for one batch of images.
 
 ## Launching and Training the Model <a class="md-anchor" id="AUTOGENERATED-launching-and-training-the-model"></a>
@@ -254,7 +254,7 @@ images.
 this loss is the sum of the cross entropy and all weight decay terms.
 
 * Keep an eye on the processing speed of a batch. The numbers shown above were
-run on a Tesla K40c. If you are running on a CPU, expect slower performance.
+obtained on a Tesla K40c. If you are running on a CPU, expect slower performance.
 
 
 > **EXERCISE:** When experimenting, it is sometimes annoying that the first