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
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  """Example of DNNClassifier for Iris plant dataset, with pipeline."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  from sklearn import cross_validation
  from sklearn.datasets import load_iris
  from sklearn.metrics import accuracy_score
  from sklearn.pipeline import Pipeline
  from sklearn.preprocessing import StandardScaler
  import tensorflow as tf
  
  from tensorflow.contrib import learn
  
  
  def main(unused_argv):
    iris = load_iris()
    x_train, x_test, y_train, y_test = cross_validation.train_test_split(
        iris.data, iris.target, test_size=0.2, random_state=42)
  
    # It's useful to scale to ensure Stochastic Gradient Descent
    # will do the right thing.
    scaler = StandardScaler()
  
    # DNN classifier.
    classifier = learn.DNNClassifier(
        feature_columns=learn.infer_real_valued_columns_from_input(x_train),
        hidden_units=[10, 20, 10], n_classes=3)
  
    pipeline = Pipeline([('scaler', scaler),
                         ('DNNclassifier', classifier)])
  
    pipeline.fit(x_train, y_train, DNNclassifier__steps=200)
  
    score = accuracy_score(y_test, pipeline.predict(x_test))
    print('Accuracy: {0:f}'.format(score))
  
  
  if __name__ == '__main__':
    tf.app.run()
  