old_contents: #  Copyright 2015-present The Scikit Flow Authors. All Rights Reserved.
  #
  #  Licensed under the Apache License, Version 2.0 (the "License");
  #  you may not use this file except in compliance with the License.
  #  You may obtain a copy of the License at
  #
  #   http://www.apache.org/licenses/LICENSE-2.0
  #
  #  Unless required by applicable law or agreed to in writing, software
  #  distributed under the License is distributed on an "AS IS" BASIS,
  #  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  #  See the License for the specific language governing permissions and
  #  limitations under the License.
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  from sklearn.pipeline import Pipeline
  from sklearn.datasets import load_iris
  from sklearn import cross_validation
  from sklearn.preprocessing import StandardScaler
  from sklearn.metrics import accuracy_score
  from tensorflow.contrib import skflow
  
  iris = load_iris()
  X_train, X_test, y_train, y_test = cross_validation.train_test_split(iris.data, iris.target,
      test_size=0.2, random_state=42)
  
  # It's useful to scale to ensure Stochastic Gradient Descent will do the right thing
  scaler = StandardScaler()
  
  # DNN classifier
  DNNclassifier = skflow.TensorFlowDNNClassifier(hidden_units=[10, 20, 10], n_classes=3, steps=200)
  
  pipeline = Pipeline([('scaler', scaler, ('DNNclassifier', DNNclassifier)])
  
  pipeline.fit(X_train, y_train)
  
  score = accuracy_score(y_test, pipeline.predict(X_test))
  
  print('Accuracy: {0:f}'.format(score))
  
new_contents: #  Copyright 2015-present The Scikit Flow Authors. All Rights Reserved.
  #
  #  Licensed under the Apache License, Version 2.0 (the "License");
  #  you may not use this file except in compliance with the License.
  #  You may obtain a copy of the License at
  #
  #   http://www.apache.org/licenses/LICENSE-2.0
  #
  #  Unless required by applicable law or agreed to in writing, software
  #  distributed under the License is distributed on an "AS IS" BASIS,
  #  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  #  See the License for the specific language governing permissions and
  #  limitations under the License.
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  from sklearn.pipeline import Pipeline
  from sklearn.datasets import load_iris
  from sklearn import cross_validation
  from sklearn.preprocessing import StandardScaler
  from sklearn.metrics import accuracy_score
  from tensorflow.contrib import skflow
  
  iris = load_iris()
  X_train, X_test, y_train, y_test = cross_validation.train_test_split(iris.data, iris.target,
      test_size=0.2, random_state=42)
  
  # It's useful to scale to ensure Stochastic Gradient Descent will do the right thing
  scaler = StandardScaler()
  
  # DNN classifier
  DNNclassifier = skflow.TensorFlowDNNClassifier(hidden_units=[10, 20, 10], n_classes=3, steps=200)
  
  pipeline = Pipeline([('scaler', scaler), ('DNNclassifier', DNNclassifier)])
  
  pipeline.fit(X_train, y_train)
  
  score = accuracy_score(y_test, pipeline.predict(X_test))
  
  print('Accuracy: {0:f}'.format(score))
  
subject: Fix pipeline example.