Update generated Python Op docs.

Change: 137866950

tensorflow/g3doc/api_docs/python/contrib.distributions.md

@@ -17325,62 +17325,6 @@ Variance.
 
 
 
-- - -
-
-### `tf.contrib.distributions.matrix_diag_transform(matrix, transform=None, name=None)` {#matrix_diag_transform}
-
-Transform diagonal of [batch-]matrix, leave rest of matrix unchanged.
-
-Create a trainable covariance defined by a Cholesky factor:
-
-```python
-# Transform network layer into 2 x 2 array.
-matrix_values = tf.contrib.layers.fully_connected(activations, 4)
-matrix = tf.reshape(matrix_values, (batch_size, 2, 2))
-
-# Make the diagonal positive.  If the upper triangle was zero, this would be a
-# valid Cholesky factor.
-chol = matrix_diag_transform(matrix, transform=tf.nn.softplus)
-
-# OperatorPDCholesky ignores the upper triangle.
-operator = OperatorPDCholesky(chol)
-```
-
-Example of heteroskedastic 2-D linear regression.
-
-```python
-# Get a trainable Cholesky factor.
-matrix_values = tf.contrib.layers.fully_connected(activations, 4)
-matrix = tf.reshape(matrix_values, (batch_size, 2, 2))
-chol = matrix_diag_transform(matrix, transform=tf.nn.softplus)
-
-# Get a trainable mean.
-mu = tf.contrib.layers.fully_connected(activations, 2)
-
-# This is a fully trainable multivariate normal!
-dist = tf.contrib.distributions.MVNCholesky(mu, chol)
-
-# Standard log loss.  Minimizing this will "train" mu and chol, and then dist
-# will be a distribution predicting labels as multivariate Gaussians.
-loss = -1 * tf.reduce_mean(dist.log_pdf(labels))
-```
-
-##### Args:
-
-
-*  <b>`matrix`</b>: Rank `R` `Tensor`, `R >= 2`, where the last two dimensions are
-    equal.
-*  <b>`transform`</b>: Element-wise function mapping `Tensors` to `Tensors`.  To
-    be applied to the diagonal of `matrix`.  If `None`, `matrix` is returned
-    unchanged.  Defaults to `None`.
-*  <b>`name`</b>: A name to give created ops.
-    Defaults to "matrix_diag_transform".
-
-##### Returns:
-
-  A `Tensor` with same shape and `dtype` as `matrix`.
-
-
 
 ### Other multivariate distributions
 
@@ -20793,6 +20737,65 @@ Variance.
 
 
 
+### Multivariate Utilities
+
+- - -
+
+### `tf.contrib.distributions.matrix_diag_transform(matrix, transform=None, name=None)` {#matrix_diag_transform}
+
+Transform diagonal of [batch-]matrix, leave rest of matrix unchanged.
+
+Create a trainable covariance defined by a Cholesky factor:
+
+```python
+# Transform network layer into 2 x 2 array.
+matrix_values = tf.contrib.layers.fully_connected(activations, 4)
+matrix = tf.reshape(matrix_values, (batch_size, 2, 2))
+
+# Make the diagonal positive.  If the upper triangle was zero, this would be a
+# valid Cholesky factor.
+chol = matrix_diag_transform(matrix, transform=tf.nn.softplus)
+
+# OperatorPDCholesky ignores the upper triangle.
+operator = OperatorPDCholesky(chol)
+```
+
+Example of heteroskedastic 2-D linear regression.
+
+```python
+# Get a trainable Cholesky factor.
+matrix_values = tf.contrib.layers.fully_connected(activations, 4)
+matrix = tf.reshape(matrix_values, (batch_size, 2, 2))
+chol = matrix_diag_transform(matrix, transform=tf.nn.softplus)
+
+# Get a trainable mean.
+mu = tf.contrib.layers.fully_connected(activations, 2)
+
+# This is a fully trainable multivariate normal!
+dist = tf.contrib.distributions.MVNCholesky(mu, chol)
+
+# Standard log loss.  Minimizing this will "train" mu and chol, and then dist
+# will be a distribution predicting labels as multivariate Gaussians.
+loss = -1 * tf.reduce_mean(dist.log_pdf(labels))
+```
+
+##### Args:
+
+
+*  <b>`matrix`</b>: Rank `R` `Tensor`, `R >= 2`, where the last two dimensions are
+    equal.
+*  <b>`transform`</b>: Element-wise function mapping `Tensors` to `Tensors`.  To
+    be applied to the diagonal of `matrix`.  If `None`, `matrix` is returned
+    unchanged.  Defaults to `None`.
+*  <b>`name`</b>: A name to give created ops.
+    Defaults to "matrix_diag_transform".
+
+##### Returns:
+
+  A `Tensor` with same shape and `dtype` as `matrix`.
+
+
+
 ## Transformed distributions
 
 - - -
@@ -23052,7 +23055,7 @@ will broadcast in the case of multidimensional sets of parameters.
 
 
 
-## Kullback Leibler Divergence
+## Kullback-Leibler Divergence
 
 - - -