Fix doc for _DNNLinearCombinedBaseEstimator.

Change: 125575345

tensorflow/contrib/learn/python/learn/estimators/dnn_linear_combined.py

@@ -80,7 +80,6 @@ class _DNNLinearCombinedBaseEstimator(estimator.BaseEstimator):
 
     Args:
       model_dir: Directory to save model parameters, graph and etc.
-      n_classes: number of target classes. Default is binary classification.
       weight_column_name: A string defining feature column name representing
         weights. It is used to down weight or boost examples during training. It
         will be multiplied by the loss of the example.
@@ -92,10 +91,10 @@ class _DNNLinearCombinedBaseEstimator(estimator.BaseEstimator):
       dnn_feature_columns: An iterable containing all the feature columns used
         by deep part of the model. All items in the set should be instances of
         classes derived from `FeatureColumn`.
-      dnn_hidden_units: List of hidden units per layer. All layers are fully
-        connected.
       dnn_optimizer: An instance of `tf.Optimizer` used to apply gradients to
         the deep part of the model. If `None`, will use an Adagrad optimizer.
+      dnn_hidden_units: List of hidden units per layer. All layers are fully
+        connected.
       dnn_activation_fn: Activation function applied to each layer. If `None`,
         will use `tf.nn.relu`.
       dnn_dropout: When not None, the probability we will drop out
@@ -485,10 +484,10 @@ class DNNLinearCombinedClassifier(_DNNLinearCombinedBaseEstimator):
       dnn_feature_columns: An iterable containing all the feature columns used
         by deep part of the model. All items in the set must be instances of
         classes derived from `FeatureColumn`.
-      dnn_hidden_units: List of hidden units per layer. All layers are fully
-        connected.
       dnn_optimizer: An instance of `tf.Optimizer` used to apply gradients to
         the deep part of the model. If `None`, will use an Adagrad optimizer.
+      dnn_hidden_units: List of hidden units per layer. All layers are fully
+        connected.
       dnn_activation_fn: Activation function applied to each layer. If `None`,
         will use `tf.nn.relu`.
       dnn_dropout: When not None, the probability we will drop out
@@ -732,10 +731,10 @@ class DNNLinearCombinedRegressor(_DNNLinearCombinedBaseEstimator):
       dnn_feature_columns: An iterable containing all the feature columns used
         by deep part of the model. All items in the set must be instances of
         classes derived from `FeatureColumn`.
-      dnn_hidden_units: List of hidden units per layer. All layers are fully
-        connected.
       dnn_optimizer: An instance of `tf.Optimizer` used to apply gradients to
         the deep part of the model. If `None`, will use an Adagrad optimizer.
+      dnn_hidden_units: List of hidden units per layer. All layers are fully
+        connected.
       dnn_activation_fn: Activation function applied to each layer. If None,
         will use `tf.nn.relu`.
       dnn_dropout: When not None, the probability we will drop out