1 files changed, 42 insertions, 40 deletions
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.Distribution.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.Distribution.md
index a2658ef44b..0076cdc4ff 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.Distribution.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.Distribution.md
@@ -6,8 +6,8 @@ A generic probability distribution base class.
 ### Subclassing
 
 Subclasses are expected to implement a leading-underscore version of the
-same-named function.  The argument signature should be identical except for
-the omission of `name="..."`.  For example, to enable `log_prob(value,
+same-named function. The argument signature should be identical except for
+the omission of `name="..."`. For example, to enable `log_prob(value,
 name="log_prob")` a subclass should implement `_log_prob(value)`.
 
 Subclasses can append to public-level docstrings by providing
@@ -20,7 +20,7 @@ def _log_prob(self, value):
 ```
 
 would add the string "Some other details." to the `log_prob` function
-docstring.  This is implemented as a simple decorator to avoid python
+docstring. This is implemented as a simple decorator to avoid python
 linter complaining about missing Args/Returns/Raises sections in the
 partial docstrings.
 
@@ -33,7 +33,7 @@ The shape of arguments to `__init__`, `cdf`, `log_cdf`, `prob`, and
 `log_prob` reflect this broadcasting, as does the return value of `sample` and
 `sample_n`.
 
-`sample_n_shape = (n,) + batch_shape + event_shape`, where `sample_n_shape` is
+`sample_n_shape = [n] + batch_shape + event_shape`, where `sample_n_shape` is
 the shape of the `Tensor` returned from `sample_n`, `n` is the number of
 samples, `batch_shape` defines how many independent distributions there are,
 and `event_shape` defines the shape of samples from each of those independent
@@ -58,19 +58,19 @@ event_shape = u.event_shape
 # `event_shape_t` is a `Tensor` which will evaluate to [].
 event_shape_t = u.event_shape_tensor()
 
-# Sampling returns a sample per distribution.  `samples` has shape
-# (5, 2, 2), which is (n,) + batch_shape + event_shape, where n=5,
-# batch_shape=(2, 2), and event_shape=().
+# Sampling returns a sample per distribution. `samples` has shape
+# [5, 2, 2], which is [n] + batch_shape + event_shape, where n=5,
+# batch_shape=[2, 2], and event_shape=[].
 samples = u.sample_n(5)
 
 # The broadcasting holds across methods. Here we use `cdf` as an example. The
 # same holds for `log_cdf` and the likelihood functions.
 
-# `cum_prob` has shape (2, 2) as the `value` argument was broadcasted to the
+# `cum_prob` has shape [2, 2] as the `value` argument was broadcasted to the
 # shape of the `Uniform` instance.
 cum_prob_broadcast = u.cdf(4.0)
 
-# `cum_prob`'s shape is (2, 2), one per distribution. No broadcasting
+# `cum_prob`'s shape is [2, 2], one per distribution. No broadcasting
 # occurred.
 cum_prob_per_dist = u.cdf([[4.0, 5.0],
                            [6.0, 7.0]])
@@ -83,9 +83,9 @@ cum_prob_invalid = u.cdf([4.0, 5.0, 6.0])
 ### Parameter values leading to undefined statistics or distributions.
 
 Some distributions do not have well-defined statistics for all initialization
-parameter values.  For example, the beta distribution is parameterized by
-positive real numbers `a` and `b`, and does not have well-defined mode if
-`a < 1` or `b < 1`.
+parameter values. For example, the beta distribution is parameterized by
+positive real numbers `concentration1` and `concentration0`, and does not have
+well-defined mode if `concentration1 < 1` or `concentration0 < 1`.
 
 The user is given the option of raising an exception or returning `NaN`.
 
@@ -122,25 +122,28 @@ Constructs the `Distribution`.
 
 
 *  <b>`dtype`</b>: The type of the event samples. `None` implies no type-enforcement.
-*  <b>`is_continuous`</b>: Python boolean. If `True` this
-    `Distribution` is continuous over its supported domain.
+*  <b>`is_continuous`</b>: Python `bool`. If `True` this `Distribution` is continuous
+    over its supported domain.
 *  <b>`reparameterization_type`</b>: Instance of `ReparameterizationType`.
     If `distributions.FULLY_REPARAMETERIZED`, this
     `Distribution` can be reparameterized in terms of some standard
     distribution with a function whose Jacobian is constant for the support
-    of the standard distribution.  If `distributions.NOT_REPARAMETERIZED`,
+    of the standard distribution. If `distributions.NOT_REPARAMETERIZED`,
     then no such reparameterization is available.
-*  <b>`validate_args`</b>: Python boolean.  Whether to validate input with asserts.
-    If `validate_args` is `False`, and the inputs are invalid,
-    correct behavior is not guaranteed.
-*  <b>`allow_nan_stats`</b>: Python boolean.  If `False`, raise an
-    exception if a statistic (e.g., mean, mode) is undefined for any batch
-    member. If True, batch members with valid parameters leading to
-    undefined statistics will return `NaN` for this statistic.
-*  <b>`parameters`</b>: Python dictionary of parameters used to instantiate this
+*  <b>`validate_args`</b>: Python `bool`, default `False`. When `True` distribution
+    parameters are checked for validity despite possibly degrading runtime
+    performance. When `False` invalid inputs may silently render incorrect
+    outputs.
+*  <b>`allow_nan_stats`</b>: Python `bool`, default `True`. When `True`, statistics
+    (e.g., mean, mode, variance) use the value "`NaN`" to indicate the
+    result is undefined. When `False`, an exception is raised if one or
+    more of the statistic's batch members are undefined.
+*  <b>`parameters`</b>: Python `dict` of parameters used to instantiate this
     `Distribution`.
-*  <b>`graph_parents`</b>: Python list of graph prerequisites of this `Distribution`.
-*  <b>`name`</b>: A name for this distribution. Default: subclass name.
+*  <b>`graph_parents`</b>: Python `list` of graph prerequisites of this
+    `Distribution`.
+*  <b>`name`</b>: Python `str` name prefixed to Ops created by this class. Default:
+    subclass name.
 
 ##### Raises:
 
@@ -152,21 +155,20 @@ Constructs the `Distribution`.
 
 #### `tf.contrib.distributions.Distribution.allow_nan_stats` {#Distribution.allow_nan_stats}
 
-Python boolean describing behavior when a stat is undefined.
+Python `bool` describing behavior when a stat is undefined.
 
-Stats return +/- infinity when it makes sense.  E.g., the variance
-of a Cauchy distribution is infinity.  However, sometimes the
-statistic is undefined, e.g., if a distribution's pdf does not achieve a
-maximum within the support of the distribution, the mode is undefined.
-If the mean is undefined, then by definition the variance is undefined.
-E.g. the mean for Student's T for df = 1 is undefined (no clear way to say
-it is either + or - infinity), so the variance = E[(X - mean)^2] is also
-undefined.
+Stats return +/- infinity when it makes sense. E.g., the variance of a
+Cauchy distribution is infinity. However, sometimes the statistic is
+undefined, e.g., if a distribution's pdf does not achieve a maximum within
+the support of the distribution, the mode is undefined. If the mean is
+undefined, then by definition the variance is undefined. E.g. the mean for
+Student's T for df = 1 is undefined (no clear way to say it is either + or -
+infinity), so the variance = E[(X - mean)**2] is also undefined.
 
 ##### Returns:
 
 
-*  <b>`allow_nan_stats`</b>: Python boolean.
+*  <b>`allow_nan_stats`</b>: Python `bool`.
 
 
 - - -
@@ -364,7 +366,7 @@ Indicates that `batch_shape == []`.
 ##### Returns:
 
 
-*  <b>`is_scalar_batch`</b>: `Boolean` `scalar` `Tensor`.
+*  <b>`is_scalar_batch`</b>: `bool` scalar `Tensor`.
 
 
 - - -
@@ -381,7 +383,7 @@ Indicates that `event_shape == []`.
 ##### Returns:
 
 
-*  <b>`is_scalar_event`</b>: `Boolean` `scalar` `Tensor`.
+*  <b>`is_scalar_event`</b>: `bool` scalar `Tensor`.
 
 
 - - -
@@ -514,8 +516,8 @@ param_shapes with static (i.e. `TensorShape`) shapes.
 
 This is a class method that describes what key/value arguments are required
 to instantiate the given `Distribution` so that a particular shape is
-returned for that instance's call to `sample()`.  Assumes that
-the sample's shape is known statically.
+returned for that instance's call to `sample()`. Assumes that the sample's
+shape is known statically.
 
 Subclasses should override class method `_param_shapes` to return
 constant-valued tensors when constant values are fed.
@@ -656,7 +658,7 @@ survival_function(x) = P[X > x]
 
 #### `tf.contrib.distributions.Distribution.validate_args` {#Distribution.validate_args}
 
-Python boolean indicated possibly expensive checks are enabled.
+Python `bool` indicating possibly expensive checks are enabled.
 
 
 - - -