Update generated Python Op docs.

Change: 124868009

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

@@ -27,10 +27,11 @@ Concrete implementation of this class should provide following functions:
 
 Initializes a BaseEstimator instance.
 
-##### Parameters:
+##### Args:
 
 
 *  <b>`model_dir`</b>: Directory to save model parameters, graph and etc.
+*  <b>`config`</b>: A RunConfig instance.
 
 
 - - -
@@ -293,16 +294,21 @@ Constructs an Estimator instance.
                    (depending on data passed to `fit`),
               * `targets` are `Tensor` or
                   `dict` of `Tensor`s (for multi-head model).
-              * `mode` represents if this training, evaluation or prediction.
-                  See `ModeKeys` for example keys.
-              * `params` is a `dict` of hyperparameters. Will receive what is
-                  passed to Estimator in `params` parameter. This allows to
-                  configure Estimators from hyper parameter tunning.
+              * `mode` represents if this training, evaluation or
+                  prediction. See `ModeKeys` for example keys.
+              * `params` is a `dict` of hyperparameters. Will receive what
+                  is passed to Estimator in `params` parameter. This allows
+                  to configure Estimators from hyper parameter tunning.
 *  <b>`model_dir`</b>: Directory to save model parameters, graph and etc.
 *  <b>`config`</b>: Configuration object.
 *  <b>`params`</b>: `dict` of hyper parameters that will be passed into `model_fn`.
           Keys are names of parameters, values are basic python types.
 
+##### Raises:
+
+
+*  <b>`ValueError`</b>: parameters of `model_fn` don't match `params`.
+
 
 - - -
 

tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.learn.BaseEstimator.md

@@ -6,14 +6,17 @@ Concrete implementation of this class should provide following functions:
   * _get_predict_ops
 
 `Estimator` implemented below is a good example of how to use this class.
-
-Parameters:
-  model_dir: Directory to save model parameters, graph and etc.
 - - -
 
 #### `tf.contrib.learn.BaseEstimator.__init__(model_dir=None, config=None)` {#BaseEstimator.__init__}
 
+Initializes a BaseEstimator instance.
 
+##### Args:
+
+
+*  <b>`model_dir`</b>: Directory to save model parameters, graph and etc.
+*  <b>`config`</b>: A RunConfig instance.
 
 
 - - -

tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.learn.Estimator.md

@@ -1,32 +1,39 @@
 Estimator class is the basic TensorFlow model trainer/evaluator.
+- - -
 
-Parameters:
-  model_fn: Model function, takes features and targets tensors or dicts of
+#### `tf.contrib.learn.Estimator.__init__(model_fn=None, model_dir=None, config=None, params=None)` {#Estimator.__init__}
+
+Constructs an Estimator instance.
+
+##### Args:
+
+
+*  <b>`model_fn`</b>: Model function, takes features and targets tensors or dicts of
             tensors and returns predictions and loss tensors.
             Supports next three signatures for the function:
               * `(features, targets) -> (predictions, loss, train_op)`
               * `(features, targets, mode) -> (predictions, loss, train_op)`
               * `(features, targets, mode, params) ->
                   (predictions, loss, train_op)`
-            Where:
+*  <b>`Where`</b>: 
               * `features` are single `Tensor` or `dict` of `Tensor`s
                    (depending on data passed to `fit`),
               * `targets` are `Tensor` or
                   `dict` of `Tensor`s (for multi-head model).
-              * `mode` represents if this training, evaluation or prediction.
-                  See `ModeKeys` for example keys.
-              * `params` is a `dict` of hyperparameters. Will receive what is
-                  passed to Estimator in `params` parameter. This allows to
-                  configure Estimators from hyper parameter tunning.
-  model_dir: Directory to save model parameters, graph and etc.
-  config: Configuration object.
-  params: `dict` of hyper parameters that will be passed into `model_fn`.
+              * `mode` represents if this training, evaluation or
+                  prediction. See `ModeKeys` for example keys.
+              * `params` is a `dict` of hyperparameters. Will receive what
+                  is passed to Estimator in `params` parameter. This allows
+                  to configure Estimators from hyper parameter tunning.
+*  <b>`model_dir`</b>: Directory to save model parameters, graph and etc.
+*  <b>`config`</b>: Configuration object.
+*  <b>`params`</b>: `dict` of hyper parameters that will be passed into `model_fn`.
           Keys are names of parameters, values are basic python types.
-- - -
 
-#### `tf.contrib.learn.Estimator.__init__(model_fn=None, model_dir=None, config=None, params=None)` {#Estimator.__init__}
+##### Raises:
 
 
+*  <b>`ValueError`</b>: parameters of `model_fn` don't match `params`.
 
 
 - - -