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
@@ -108,9 +107,9 @@ class _DNNLinearCombinedBaseEstimator(estimator.BaseEstimator):
         residual after centered bias.
       config: RunConfig object to configure the runtime settings.
 
-      Raises:
-        ValueError: If both linear_feature_columns and dnn_features_columns are
-          empty at the same time.
+    Raises:
+      ValueError: If both linear_feature_columns and dnn_features_columns are
+        empty at the same time.
     """
     super(_DNNLinearCombinedBaseEstimator, self).__init__(model_dir=model_dir,
                                                           config=config)
@@ -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
@@ -501,10 +500,10 @@ class DNNLinearCombinedClassifier(_DNNLinearCombinedBaseEstimator):
         residual after centered bias.
       config: RunConfig object to configure the runtime settings.
 
-      Raises:
-        ValueError: If both linear_feature_columns and dnn_features_columns are
-          empty at the same time.
-        ValueError: If both n_classes < 2.
+    Raises:
+      ValueError: If both linear_feature_columns and dnn_features_columns are
+        empty at the same time.
+      ValueError: If both n_classes < 2.
     """
 
     if n_classes < 2:
@@ -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
@@ -748,9 +747,9 @@ class DNNLinearCombinedRegressor(_DNNLinearCombinedBaseEstimator):
         residual after centered bias.
       config: RunConfig object to configure the runtime settings.
 
-      Raises:
-        ValueError: If both linear_feature_columns and dnn_features_columns are
-          empty at the same time.
+    Raises:
+      ValueError: If both linear_feature_columns and dnn_features_columns are
+        empty at the same time.
     """
     super(DNNLinearCombinedRegressor, self).__init__(
         model_dir=model_dir,