content: #  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
  
  import tensorflow as tf
  
  import random
  
  from tensorflow.contrib.learn.python import learn
  from tensorflow.contrib.learn.python.learn import datasets
  from tensorflow.contrib.learn.python.learn.estimators._sklearn import accuracy_score
  from tensorflow.contrib.learn.python.learn.estimators._sklearn import train_test_split
  
  
  class CustomDecayTest(tf.test.TestCase):
  
    def testIrisExponentialDecay(self):
      random.seed(42)
  
      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)
  
      # setup exponential decay function
      def exp_decay(global_step):
        return tf.train.exponential_decay(learning_rate=0.1,
                                          global_step=global_step,
                                          decay_steps=100,
                                          decay_rate=0.001)
  
      classifier = learn.TensorFlowDNNClassifier(hidden_units=[10, 20, 10],
                                                 n_classes=3,
                                                 steps=500,
                                                 learning_rate=exp_decay)
      classifier.fit(X_train, y_train)
      score = accuracy_score(y_test, classifier.predict(X_test))
  
      self.assertGreater(score, 0.65, "Failed with score = {0}".format(score))
  
  
  if __name__ == "__main__":
    tf.test.main()
  