Adding nav entries for Layers tutorial, and making a few small formatting fixes to it.

Change: 144588235

tensorflow/g3doc/tutorials/index.md

@@ -8,37 +8,33 @@ digit images.
 
 ### MNIST For ML Beginners
 
-If you're new to machine learning, we recommend starting here.  You'll learn
+If you're new to machine learning, we recommend starting here. You'll learn
 about a classic problem, handwritten digit classification (MNIST), and get a
 gentle introduction to multiclass classification.
 
 [View Tutorial](../tutorials/mnist/beginners/index.md)
 
-
 ### Deep MNIST for Experts
 
 If you're already familiar with other deep learning software packages, and are
-already familiar with MNIST, this tutorial will give you a very brief primer
-on TensorFlow.
+already familiar with MNIST, this tutorial will give you a very brief primer on
+TensorFlow.
 
 [View Tutorial](../tutorials/mnist/pros/index.md)
 
 ### TensorFlow Mechanics 101
 
 This is a technical tutorial, where we walk you through the details of using
-TensorFlow infrastructure to train models at scale.  We use MNIST as the
-example.
+TensorFlow infrastructure to train models at scale. We use MNIST as the example.
 
 [View Tutorial](../tutorials/mnist/tf/index.md)
 
-
 ## Easy ML with tf.contrib.learn
 
 ### tf.contrib.learn Quickstart
 
 A quick introduction to tf.contrib.learn, a high-level API for TensorFlow.
-Build, train, and evaluate a neural network with just a few lines of
-code.
+Build, train, and evaluate a neural network with just a few lines of code.
 
 [View Tutorial](../tutorials/tflearn/index.md)
 
@@ -73,19 +69,27 @@ Monitor API to audit the in-progress training of a neural network.
 ### Building Input Functions with tf.contrib.learn
 
 This tutorial introduces you to creating input functions in tf.contrib.learn,
-and walks you through implementing an `input_fn` to train a neural network
-for predicting median house values.
+and walks you through implementing an `input_fn` to train a neural network for
+predicting median house values.
 
 [View Tutorial](../tutorials/input_fn/index.md)
 
 ### Creating Estimators in tf.contrib.learn
 
-This tutorial covers how to create your own `Estimator` using the building blocks
-provided in tf.contrib.learn. You'll build a model to predict the ages of abalones
-based on their physical measurements.
+This tutorial covers how to create your own `Estimator` using the building
+blocks provided in tf.contrib.learn. You'll build a model to predict the ages of
+abalones based on their physical measurements.
 
 [View Tutorial](../tutorials/estimators/index.md)
 
+### A Guide to TF Layers: Building a Convolutional Neural Network
+
+This tutorial introduces you to building neural networks in TensorFlow using the
+`tf.layers` module. You'll build a convolutional neural network `Estimator` to
+recognize the handwritten digits in the MNIST data set.
+
+[View Tutorial](../tutorials/layers/index.md)
+
 ## TensorFlow Serving
 
 ### TensorFlow Serving
@@ -95,7 +99,6 @@ serving machine learning models, designed for production environments.
 
 [View Tutorial](../tutorials/tfserve/index.md)
 
-
 ## Image Processing
 
 ### Convolutional Neural Networks
@@ -109,8 +112,8 @@ representations of visual content.
 
 ### Image Recognition
 
-How to run object recognition using a convolutional neural network
-trained on ImageNet Challenge data and label set.
+How to run object recognition using a convolutional neural network trained on
+ImageNet Challenge data and label set.
 
 [View Tutorial](../tutorials/image_recognition/index.md)
 
@@ -120,8 +123,8 @@ Building on the Inception recognition model, we will release a TensorFlow
 version of the [Deep Dream](https://github.com/google/deepdream) neural network
 visual hallucination software.
 
-[View Tutorial](https://nbviewer.jupyter.org/github/tensorflow/tensorflow/blob/master/tensorflow/examples/tutorials/deepdream/deepdream.ipynb)
-
+[View
+Tutorial](https://nbviewer.jupyter.org/github/tensorflow/tensorflow/blob/master/tensorflow/examples/tutorials/deepdream/deepdream.ipynb)
 
 ## Language and Sequence Processing
 
@@ -138,14 +141,14 @@ embeddings).
 ### Recurrent Neural Networks
 
 An introduction to RNNs, wherein we train an LSTM network to predict the next
-word in an English sentence.  (A task sometimes called language modeling.)
+word in an English sentence. (A task sometimes called language modeling.)
 
 [View Tutorial](../tutorials/recurrent/index.md)
 
 ### Sequence-to-Sequence Models
 
 A follow on to the RNN tutorial, where we assemble a sequence-to-sequence model
-for machine translation.  You will learn to build your own English-to-French
+for machine translation. You will learn to build your own English-to-French
 translator, entirely machine learned, end-to-end.
 
 [View Tutorial](../tutorials/seq2seq/index.md)
@@ -157,19 +160,18 @@ TensorFlow.
 
 [View Tutorial](../tutorials/syntaxnet/index.md)
 
-
 ## Non-ML Applications
 
 ### Mandelbrot Set
 
 TensorFlow can be used for computation that has nothing to do with machine
-learning.  Here's a naive implementation of Mandelbrot set visualization.
+learning. Here's a naive implementation of Mandelbrot set visualization.
 
 [View Tutorial](../tutorials/mandelbrot/index.md)
 
 ### Partial Differential Equations
 
-As another example of non-machine learning computation, we offer an example of
-a naive PDE simulation of raindrops landing on a pond.
+As another example of non-machine learning computation, we offer an example of a
+naive PDE simulation of raindrops landing on a pond.
 
 [View Tutorial](../tutorials/pdes/index.md)

tensorflow/g3doc/tutorials/layers/index.md

@@ -45,7 +45,7 @@ evaluate the convolutional neural network. The complete, final code can be
 here](https://www.tensorflow.org/code/tensorflow/examples/tutorials/layers/cnn_mnist.py).
 
 <p class="note"><b>NOTE:</b> Before proceeding, make sure you've
-<a href="https://www.tensorflow.org/get_started/os_setup">installed the latest
+<a href="../../get_started/os_setup.md">installed the latest
 version of TensorFlow</a> on your machine.</p>
 
 ## Intro to Convolutional Neural Networks
@@ -87,9 +87,9 @@ is equal to 1). We can interpret the softmax values for a given image as
 relative measurements of how likely it is that the image falls into each target
 class.
 
-NOTE: For a more comprehensive walkthrough of CNN architecture, see Stanford
-University's [Convolutional Neural Networks for Visual Recognition course
-materials](http://cs231n.github.io/convolutional-networks/).
+<p class="note"><b>NOTE:</b> For a more comprehensive walkthrough of CNN
+architecture, see Stanford University's <a href="http://cs231n.github.io/convolutional-networks/">
+Convolutional Neural Networks for Visual Recognition course materials</a>.</p>
 
 ## Building the CNN MNIST Classifier {#building-cnn-classifier}
 
@@ -506,7 +506,7 @@ if mode == learn.ModeKeys.TRAIN:
 <p class="note"><b>NOTE:</b> For a more in-depth look at configuring training ops for Estimator model
 functions, see <a href="../estimators/index.md#defining_the_training_op_for_the_model">"Defining the training op for the
 model"</a> in the
-<a href="../estimators/index.md">"Creating Estimations in tf.contrib.learn"]</a> tutorial.</p>
+<a href="../estimators/index.md">"Creating Estimations in tf.contrib.learn"</a> tutorial.</p>
 
 ### Generate Predictions {#generate-predictions}
 
@@ -541,7 +541,7 @@ using [`tf.nn.softmax()`](../../api_docs/python/nn.md#softmax):
 tf.nn.softmax(logits, name="softmax_tensor")
 ```
 
-<p class="note"><b>NOTE:</b We use the `name` argument to explicitly name this operation `softmax_tensor`, so we can reference it later. (We'll set up logging for the softmax values in <a href="#set-up-a-logging-hook">Set Up a Logging Hook</a>.)</p>
+<p class="note"><b>NOTE:</b> We use the `name` argument to explicitly name this operation `softmax_tensor`, so we can reference it later. (We'll set up logging for the softmax values in <a href="#set-up-a-logging-hook">Set Up a Logging Hook</a>.)</p>
 
 We compile our predictions in a dict as follows:
 

tensorflow/g3doc/tutorials/leftnav_files

@@ -10,6 +10,7 @@ wide_and_deep/index.md
 monitors/index.md
 input_fn/index.md
 estimators/index.md
+layers/index.md
 ### TensorFlow Serving
 tfserve/index.md
 ### Image Processing