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, metrics
  from sklearn.cross_validation import train_test_split
  
  from tensorflow.contrib import learn
  
  
  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)
  
  X_train, X_val, y_train, y_val = train_test_split(X_train, y_train,
                                                    test_size=0.2, random_state=42)
  val_monitor = learn.monitors.ValidationMonitor(X_val, y_val,
                                                 early_stopping_rounds=200,
                                                 n_classes=3)
  
  # classifier with early stopping on training data
  classifier1 = learn.TensorFlowDNNClassifier(hidden_units=[10, 20, 10],
                                               n_classes=3, steps=2000)
  classifier1.fit(X_train, y_train, logdir='/tmp/iris_model/')
  score1 = metrics.accuracy_score(y_test, classifier1.predict(X_test))
  
  # classifier with early stopping on validation data
  classifier2 = learn.TensorFlowDNNClassifier(hidden_units=[10, 20, 10],
                                               n_classes=3, steps=2000)
  classifier2.fit(X_train, y_train, val_monitor, logdir='/tmp/iris_model_val/')
  score2 = metrics.accuracy_score(y_test, classifier2.predict(X_test))
  
  # in many applications, the score is improved by using early stopping on val data
  print(score2 > score1)
  
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, metrics
  from sklearn.cross_validation import train_test_split
  
  from tensorflow.contrib import learn
  
  
  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)
  
  X_train, X_val, y_train, y_val = train_test_split(X_train, y_train,
                                                    test_size=0.2, random_state=42)
  val_monitor = learn.monitors.ValidationMonitor(X_val, y_val,
                                                 early_stopping_rounds=200)
  
  # classifier with early stopping on training data
  classifier1 = learn.TensorFlowDNNClassifier(hidden_units=[10, 20, 10],
                                               n_classes=3, steps=2000)
  classifier1.fit(X_train, y_train, logdir='/tmp/iris_model/')
  score1 = metrics.accuracy_score(y_test, classifier1.predict(X_test))
  
  # classifier with early stopping on validation data
  classifier2 = learn.TensorFlowDNNClassifier(hidden_units=[10, 20, 10],
                                               n_classes=3, steps=2000)
  classifier2.fit(X_train, y_train, val_monitor, logdir='/tmp/iris_model_val/')
  score2 = metrics.accuracy_score(y_test, classifier2.predict(X_test))
  
  # in many applications, the score is improved by using early stopping on val data
  print(score2 > score1)
  
subject: Fix lint-caught error in an skflow example.