TensorFlow documentation fixes

Change: 155029319

tensorflow/contrib/layers/python/layers/layers.py

@@ -844,7 +844,7 @@ def convolution(inputs,
   variable would be created and added the activations. Finally, if
   `activation_fn` is not `None`, it is applied to the activations as well.
 
-  Performs a'trous convolution with input stride/dilation rate equal to `rate`
+  Performs atrous convolution with input stride/dilation rate equal to `rate`
   if a value > 1 for any dimension of `rate` is specified.  In this case
   `stride` values != 1 are not supported.
 
@@ -870,7 +870,7 @@ def convolution(inputs,
       "NCW".  For N=2, the valid values are "NHWC" (default) and "NCHW".
       For N=3, the valid values are "NDHWC" (default) and "NCDHW".
     rate: A sequence of N positive integers specifying the dilation rate to use
-      for a'trous convolution.  Can be a single integer to specify the same
+      for atrous convolution.  Can be a single integer to specify the same
       value for all spatial dimensions.  Specifying any `rate` value != 1 is
       incompatible with specifying any `stride` value != 1.
     activation_fn: Activation function. The default value is a ReLU function.
@@ -1865,7 +1865,7 @@ def separable_convolution2d(
       depthwise convolution stride. Can be an int if both strides are the same.
     padding: One of 'VALID' or 'SAME'.
     rate: A list of length 2: [rate_height, rate_width], specifying the dilation
-      rates for a'trous convolution. Can be an int if both rates are the same.
+      rates for atrous convolution. Can be an int if both rates are the same.
       If any value is larger than one, then both stride values need to be one.
     activation_fn: Activation function. The default value is a ReLU function.
       Explicitly set it to None to skip it and maintain a linear activation.

tensorflow/docs_src/performance/index.md

@@ -9,7 +9,7 @@ deeper with techniques detailed in @{$performance_models$High-Performance Models
     practices for optimizing your TensorFlow code.
 
   * @{$performance_models$High-Performance Models}, which contains a collection
-    advanced techniques to build highly scalable models targeting different
+    of advanced techniques to build highly scalable models targeting different
     system types and network topologies.
 
   * @{$benchmarks$Benchmarks}, which contains a collection of benchmark

tensorflow/docs_src/programmers_guide/variable_scope.md

@@ -5,7 +5,7 @@ in the way described in the @{$variables$Variables HowTo}.
 But when building complex models you often need to share large sets of
 variables and you might want to initialize all of them in one place.
 This tutorial shows how this can be done using `tf.variable_scope()` and
-the `tf.get_variable()`.
+`tf.get_variable()`.
 
 ## The Problem
 
@@ -368,6 +368,6 @@ sequence-to-sequence models.
 
 File | What's in it?
 --- | ---
-`models/tutorials/image/cifar10/cifar10.py` | Model for detecting objects in images.
+`tutorials/image/cifar10/cifar10.py` | Model for detecting objects in images.
-`models/tutorials/rnn/rnn_cell.py` | Cell functions for recurrent neural networks.
+`tutorials/rnn/rnn_cell.py` | Cell functions for recurrent neural networks.
-`models/tutorials/rnn/seq2seq.py` | Functions for building sequence-to-sequence models.
+`tutorials/rnn/seq2seq.py` | Functions for building sequence-to-sequence models.

tensorflow/docs_src/tutorials/deep_cnn.md

@@ -83,7 +83,7 @@ for details.  It consists of 1,068,298 learnable parameters and requires about
 ## Code Organization
 
 The code for this tutorial resides in
-[`tensorflow_models/tutorials/image/cifar10/`](https://github.com/tensorflow/models/tree/master/tutorials/image/cifar10/).
+[`models/tutorials/image/cifar10/`](https://github.com/tensorflow/models/tree/master/tutorials/image/cifar10/).
 
 File | Purpose
 --- | ---

tensorflow/python/layers/normalization.py

@@ -400,7 +400,9 @@ def batch_normalization(inputs,
     training: Either a Python boolean, or a TensorFlow boolean scalar tensor
       (e.g. a placeholder). Whether to return the output in training mode
       (normalized with statistics of the current batch) or in inference mode
-      (normalized with moving statistics).
+      (normalized with moving statistics). **NOTE**: make sure to set this
+      parameter correctly, or else your training/inference will not work
+      properly.
     trainable: Boolean, if `True` also add variables to the graph collection
       `GraphKeys.TRAINABLE_VARIABLES` (see tf.Variable).
     name: String, the name of the layer.

tensorflow/python/ops/variable_scope.py

@@ -1292,7 +1292,7 @@ def _pure_variable_scope(name_or_scope,
       well-defined semantics. Defaults to False (will later change to True).
 
   Yields:
-    A scope that can be to captured and reused.
+    A scope that can be captured and reused.
 
   Raises:
     ValueError: when trying to reuse within a create scope, or create within