content: #  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 early stopping."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import shutil
  
  from sklearn import datasets
  from sklearn import metrics
  from sklearn.cross_validation import train_test_split
  import tensorflow as tf
  
  learn = tf.contrib.learn
  
  
  def clean_folder(folder):
    """Cleans the given folder if it exists."""
    try:
      shutil.rmtree(folder)
    except OSError:
      pass
  
  
  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)
  
    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)
  
    model_dir = '/tmp/iris_model'
    clean_folder(model_dir)
  
    # classifier with early stopping on training data
    classifier1 = learn.DNNClassifier(
        feature_columns=learn.infer_real_valued_columns_from_input(x_train),
        hidden_units=[10, 20, 10],
        n_classes=3,
        model_dir=model_dir)
    classifier1.fit(x=x_train, y=y_train, steps=2000)
    predictions1 = list(classifier1.predict(x_test, as_iterable=True))
    score1 = metrics.accuracy_score(y_test, predictions1)
  
    model_dir = '/tmp/iris_model_val'
    clean_folder(model_dir)
  
    # classifier with early stopping on validation data, save frequently for
    # monitor to pick up new checkpoints.
    classifier2 = learn.DNNClassifier(
        feature_columns=learn.infer_real_valued_columns_from_input(x_train),
        hidden_units=[10, 20, 10],
        n_classes=3,
        model_dir=model_dir,
        config=tf.contrib.learn.RunConfig(save_checkpoints_secs=1))
    classifier2.fit(x=x_train, y=y_train, steps=2000, monitors=[val_monitor])
    predictions2 = list(classifier2.predict(x_test, as_iterable=True))
    score2 = metrics.accuracy_score(y_test, predictions2)
  
    # In many applications, the score is improved by using early stopping
    print('score1: ', score1)
    print('score2: ', score2)
    print('score2 > score1: ', score2 > score1)
  
  
  if __name__ == '__main__':
    tf.app.run()
  