old_contents: #  Copyright 2016 The TensorFlow 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.
  """Example of DNNRegressor for Housing dataset."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from sklearn import cross_validation
  from sklearn import metrics
  from sklearn import preprocessing
  import tensorflow as tf
  
  
  def main(unused_argv):
    # Load dataset
    boston = tf.contrib.learn.datasets.load_dataset('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.
    feature_columns = tf.contrib.learn.infer_real_valued_columns_from_input(
        x_train)
    regressor = tf.contrib.learn.DNNRegressor(
        feature_columns=feature_columns, hidden_units=[10, 10])
  
    # Fit
    regressor.fit(x_train, y_train, steps=5000, batch_size=1)
  
    # Predict and score
    y_predicted = list(
        regressor.predict(
            scaler.transform(x_test), as_iterable=True))
    score = metrics.mean_squared_error(y_predicted, y_test)
  
    print('MSE: {0:f}'.format(score))
  
  
  if __name__ == '__main__':
    tf.app.run()
  
new_contents: #  Copyright 2016 The TensorFlow 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.
  """Example of DNNRegressor for Housing dataset."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from sklearn import cross_validation
  from sklearn import metrics
  from sklearn import preprocessing
  import tensorflow as tf
  
  
  def main(unused_argv):
    # Load dataset
    boston = tf.contrib.learn.datasets.load_dataset('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.
    feature_columns = tf.contrib.learn.infer_real_valued_columns_from_input(
        x_train)
    regressor = tf.contrib.learn.DNNRegressor(
        feature_columns=feature_columns, hidden_units=[10, 10])
  
    # Fit
    regressor.fit(x_train, y_train, steps=5000, batch_size=1)
    
    # Transform
    x_transformed = scaler.transform(x_test)
    
    # Predict and score
    y_predicted = list(regressor.predict(x_transformed, as_iterable=True))
    score = metrics.mean_squared_error(y_predicted, y_test)
  
    print('MSE: {0:f}'.format(score))
  
  
  if __name__ == '__main__':
    tf.app.run()
  
subject: Update boston.py (#7566)