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 import datasets, cross_validation, metrics
  from sklearn import preprocessing
  
  from tensorflow.contrib import skflow
  
  # Load dataset
  boston = datasets.load_boston()
  X, y = boston.data, boston.target
  
  # Split dataset into train / test
  X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y,
      test_size=0.2, random_state=42)
  
  # Scale data (training set) to 0 mean and unit standard deviation.
  scaler = preprocessing.StandardScaler()
  X_train = scaler.fit_transform(X_train)
  
  # Build 2 layer fully connected DNN with 10, 10 units respectively.
  regressor = skflow.TensorFlowDNNRegressor(hidden_units=[10, 10],
      steps=5000, learning_rate=0.1, batch_size=1)
  
  # Fit
  regressor.fit(X_train, y_train)
  
  # Predict and score
  score = metrics.mean_squared_error(regressor.predict(scaler.fit_transform(X_test)), y_test)
  
  print('MSE: {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 import datasets, cross_validation, metrics
  from sklearn import preprocessing
  
  from tensorflow.contrib import skflow
  
  # Load dataset
  boston = datasets.load_boston()
  X, y = boston.data, boston.target
  
  # Split dataset into train / test
  X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y,
      test_size=0.2, random_state=42)
  
  # Scale data (training set) to 0 mean and unit standard deviation.
  scaler = preprocessing.StandardScaler()
  X_train = scaler.fit_transform(X_train)
  
  # Build 2 layer fully connected DNN with 10, 10 units respectively.
  regressor = skflow.TensorFlowDNNRegressor(hidden_units=[10, 10],
      steps=5000, learning_rate=0.1, batch_size=1)
  
  # Fit
  regressor.fit(X_train, y_train)
  
  # Predict and score
  score = metrics.mean_squared_error(regressor.predict(scaler.transform(X_test)), y_test)
  
  print('MSE: {0:f}'.format(score))
  
subject: fix scaler transform boston skflow example (#2152)