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.
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  from sklearn import datasets
  from sklearn import metrics
  from sklearn.cross_validation import train_test_split
  import tensorflow as tf
  
  
  def optimizer_exp_decay():
    global_step = tf.contrib.framework.get_or_create_global_step()
    learning_rate = tf.train.exponential_decay(
        learning_rate=0.1, global_step=global_step,
        decay_steps=100, decay_rate=0.001)
    return tf.train.AdagradOptimizer(learning_rate=learning_rate)
  
  
  def main(unused_argv):
    iris = datasets.load_iris()
    x_train, x_test, y_train, y_test = train_test_split(
        iris.data, iris.target, test_size=0.2, random_state=42)
  
    feature_columns = tf.contrib.learn.infer_real_valued_columns_from_input(
        x_train)
    classifier = tf.contrib.learn.DNNClassifier(feature_columns=feature_columns,
                                                hidden_units=[10, 20, 10],
                                                n_classes=3,
                                                optimizer=optimizer_exp_decay)
  
    classifier.fit(x_train, y_train, steps=800)
    predictions = list(classifier.predict(x_test, as_iterable=True))
    score = metrics.accuracy_score(y_test, predictions)
    print('Accuracy: {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 DNNClassifier for Iris plant dataset, with exponential decay."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  from sklearn import datasets
  from sklearn import metrics
  from sklearn.cross_validation import train_test_split
  import tensorflow as tf
  
  
  def optimizer_exp_decay():
    global_step = tf.contrib.framework.get_or_create_global_step()
    learning_rate = tf.train.exponential_decay(
        learning_rate=0.1, global_step=global_step,
        decay_steps=100, decay_rate=0.001)
    return tf.train.AdagradOptimizer(learning_rate=learning_rate)
  
  
  def main(unused_argv):
    iris = datasets.load_iris()
    x_train, x_test, y_train, y_test = train_test_split(
        iris.data, iris.target, test_size=0.2, random_state=42)
  
    feature_columns = tf.contrib.learn.infer_real_valued_columns_from_input(
        x_train)
    classifier = tf.contrib.learn.DNNClassifier(feature_columns=feature_columns,
                                                hidden_units=[10, 20, 10],
                                                n_classes=3,
                                                optimizer=optimizer_exp_decay)
  
    classifier.fit(x_train, y_train, steps=800)
    predictions = list(classifier.predict(x_test, as_iterable=True))
    score = metrics.accuracy_score(y_test, predictions)
    print('Accuracy: {0:f}'.format(score))
  
  
  if __name__ == '__main__':
    tf.app.run()
  
subject: add code description (#9106)