Update generated Python Op docs.

Change: 128198937

6 files changed, 67 insertions, 17 deletions

diff --git a/tensorflow/g3doc/api_docs/python/contrib.distributions.md b/tensorflow/g3doc/api_docs/python/contrib.distributions.md
index 1ad6445284..289dc49773 100644
--- a/tensorflow/g3doc/api_docs/python/contrib.distributions.md
+++ b/tensorflow/g3doc/api_docs/python/contrib.distributions.md
@@ -6320,9 +6320,34 @@ Boolean describing behavior on invalid input.
 
 - - -
 
-#### `tf.contrib.distributions.DirichletMultinomial.variance(name='variance')` {#DirichletMultinomial.variance}
+#### `tf.contrib.distributions.DirichletMultinomial.variance(name='mean')` {#DirichletMultinomial.variance}
 
-Variance of the distribution.
+Class variances for every batch member.
+
+The variance for each batch member is defined as the following:
+
+```
+Var(X_j) = n * alpha_j / alpha_0 * (1 - alpha_j / alpha_0) *
+  (n + alpha_0) / (1 + alpha_0)
+```
+
+where `alpha_0 = sum_j alpha_j`.
+
+The covariance between elements in a batch is defined as:
+
+```
+Cov(X_i, X_j) = -n * alpha_i * alpha_j / alpha_0 ** 2 *
+  (n + alpha_0) / (1 + alpha_0)
+```
+
+##### Args:
+
+
+*  <b>`name`</b>: The name for this op.
+
+##### Returns:
+
+  A `Tensor` representing the variances for each batch member.
 
 
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.nn.moments.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.nn.moments.md
index 704bb5ba49..700d8fcff2 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.nn.moments.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.nn.moments.md
@@ -21,8 +21,8 @@ When using these moments for batch normalization (see
 *  <b>`shift`</b>: A `Tensor` containing the value by which to shift the data for
     numerical stability, or `None` if no shift is to be performed. A shift
     close to the true mean provides the most numerically stable results.
-*  <b>`keep_dims`</b>: produce moments with the same dimensionality as the input.
 *  <b>`name`</b>: Name used to scope the operations that compute the moments.
+*  <b>`keep_dims`</b>: produce moments with the same dimensionality as the input.
 
 ##### Returns:
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.DirichletMultinomial.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.DirichletMultinomial.md
index f63ce5e9e6..f5a88c11dd 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.DirichletMultinomial.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.DirichletMultinomial.md
@@ -397,8 +397,33 @@ Boolean describing behavior on invalid input.
 
 - - -
 
-#### `tf.contrib.distributions.DirichletMultinomial.variance(name='variance')` {#DirichletMultinomial.variance}
+#### `tf.contrib.distributions.DirichletMultinomial.variance(name='mean')` {#DirichletMultinomial.variance}
 
-Variance of the distribution.
+Class variances for every batch member.
+
+The variance for each batch member is defined as the following:
+
+```
+Var(X_j) = n * alpha_j / alpha_0 * (1 - alpha_j / alpha_0) *
+  (n + alpha_0) / (1 + alpha_0)
+```
+
+where `alpha_0 = sum_j alpha_j`.
+
+The covariance between elements in a batch is defined as:
+
+```
+Cov(X_i, X_j) = -n * alpha_i * alpha_j / alpha_0 ** 2 *
+  (n + alpha_0) / (1 + alpha_0)
+```
+
+##### Args:
+
+
+*  <b>`name`</b>: The name for this op.
+
+##### Returns:
+
+  A `Tensor` representing the variances for each batch member.
 
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.nn.depthwise_conv2d.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.nn.depthwise_conv2d.md
index 081897c19e..5fee10cf40 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.nn.depthwise_conv2d.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.nn.depthwise_conv2d.md
@@ -27,7 +27,7 @@ same horizontal and vertical strides, `strides = [1, stride, stride, 1]`.
     `[filter_height, filter_width, in_channels, channel_multiplier]`.
 *  <b>`strides`</b>: 1-D of size 4.  The stride of the sliding window for each
     dimension of `input`.
-*  <b>`padding`</b>: A string, either `'VALID'` or `'SAME'`.  The padding algorithm.
+*  <b>`padding`</b>: A string, either `'VALID'` or `'SAME'`. The padding algorithm.
     See the [comment here](https://www.tensorflow.org/api_docs/python/nn.html#convolution)
 *  <b>`name`</b>: A name for this operation (optional).
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.nn.batch_normalization.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.nn.batch_normalization.md
index eda1d7d053..1432f1ce2a 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.nn.batch_normalization.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.nn.batch_normalization.md
@@ -4,9 +4,9 @@ Batch normalization.
 
 As described in http://arxiv.org/abs/1502.03167.
 Normalizes a tensor by `mean` and `variance`, and applies (optionally) a
-`scale` \\(\gamma\\) to it, as well as an `offset` \\(\beta\\):
+`scale` \\\\(\gamma\\\\) to it, as well as an `offset` \\\\(\\beta\\\\):
 
-\\(\frac{\gamma(x-\mu)}{\sigma}+\beta\\)
+\\\\(\\frac{\gamma(x-\mu)}{\sigma}+\\beta\\\\)
 
 `mean`, `variance`, `offset` and `scale` are all expected to be of one of two
 shapes:
@@ -33,9 +33,9 @@ shapes:
 *  <b>`x`</b>: Input `Tensor` of arbitrary dimensionality.
 *  <b>`mean`</b>: A mean `Tensor`.
 *  <b>`variance`</b>: A variance `Tensor`.
-*  <b>`offset`</b>: An offset `Tensor`, often denoted \\(\beta\\) in equations, or
+*  <b>`offset`</b>: An offset `Tensor`, often denoted \\\\(\\beta\\\\) in equations, or
     None. If present, will be added to the normalized tensor.
-*  <b>`scale`</b>: A scale `Tensor`, often denoted \\(\gamma\\) in equations, or
+*  <b>`scale`</b>: A scale `Tensor`, often denoted \\\\(\gamma\\\\) in equations, or
     `None`. If present, the scale is applied to the normalized tensor.
 *  <b>`variance_epsilon`</b>: A small float number to avoid dividing by 0.
 *  <b>`name`</b>: A name for this operation (optional).
diff --git a/tensorflow/g3doc/api_docs/python/nn.md b/tensorflow/g3doc/api_docs/python/nn.md
index 69c7334b57..c0e5477f82 100644
--- a/tensorflow/g3doc/api_docs/python/nn.md
+++ b/tensorflow/g3doc/api_docs/python/nn.md
@@ -7,7 +7,7 @@ Note: Functions taking `Tensor` arguments can also take anything accepted by
 
 [TOC]
 
-## Activation Functions
+## Activation Functions.
 
 The activation ops provide different types of nonlinearities for use in neural
 networks.  These include smooth nonlinearities (`sigmoid`, `tanh`, `elu`,
@@ -367,7 +367,7 @@ same horizontal and vertical strides, `strides = [1, stride, stride, 1]`.
     `[filter_height, filter_width, in_channels, channel_multiplier]`.
 *  <b>`strides`</b>: 1-D of size 4.  The stride of the sliding window for each
     dimension of `input`.
-*  <b>`padding`</b>: A string, either `'VALID'` or `'SAME'`.  The padding algorithm.
+*  <b>`padding`</b>: A string, either `'VALID'` or `'SAME'`. The padding algorithm.
     See the [comment here](https://www.tensorflow.org/api_docs/python/nn.html#convolution)
 *  <b>`name`</b>: A name for this operation (optional).
 
@@ -1058,8 +1058,8 @@ When using these moments for batch normalization (see
 *  <b>`shift`</b>: A `Tensor` containing the value by which to shift the data for
     numerical stability, or `None` if no shift is to be performed. A shift
     close to the true mean provides the most numerically stable results.
-*  <b>`keep_dims`</b>: produce moments with the same dimensionality as the input.
 *  <b>`name`</b>: Name used to scope the operations that compute the moments.
+*  <b>`keep_dims`</b>: produce moments with the same dimensionality as the input.
 
 ##### Returns:
 
@@ -2411,9 +2411,9 @@ Batch normalization.
 
 As described in http://arxiv.org/abs/1502.03167.
 Normalizes a tensor by `mean` and `variance`, and applies (optionally) a
-`scale` \\(\gamma\\) to it, as well as an `offset` \\(\beta\\):
+`scale` \\\\(\gamma\\\\) to it, as well as an `offset` \\\\(\\beta\\\\):
 
-\\(\frac{\gamma(x-\mu)}{\sigma}+\beta\\)
+\\\\(\\frac{\gamma(x-\mu)}{\sigma}+\\beta\\\\)
 
 `mean`, `variance`, `offset` and `scale` are all expected to be of one of two
 shapes:
@@ -2440,9 +2440,9 @@ shapes:
 *  <b>`x`</b>: Input `Tensor` of arbitrary dimensionality.
 *  <b>`mean`</b>: A mean `Tensor`.
 *  <b>`variance`</b>: A variance `Tensor`.
-*  <b>`offset`</b>: An offset `Tensor`, often denoted \\(\beta\\) in equations, or
+*  <b>`offset`</b>: An offset `Tensor`, often denoted \\\\(\\beta\\\\) in equations, or
     None. If present, will be added to the normalized tensor.
-*  <b>`scale`</b>: A scale `Tensor`, often denoted \\(\gamma\\) in equations, or
+*  <b>`scale`</b>: A scale `Tensor`, often denoted \\\\(\gamma\\\\) in equations, or
     `None`. If present, the scale is applied to the normalized tensor.
 *  <b>`variance_epsilon`</b>: A small float number to avoid dividing by 0.
 *  <b>`name`</b>: A name for this operation (optional).