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, h5 format."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  from sklearn import cross_validation
  from sklearn import metrics
  from tensorflow.contrib import learn
  import h5py  # pylint: disable=g-bad-import-order
  
  # Load dataset.
  iris = learn.datasets.load_dataset('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)
  
  # Note that we are saving and load iris data as h5 format as a simple
  # demonstration here.
  h5f = h5py.File('test_hdf5.h5', 'w')
  h5f.create_dataset('X_train', data=x_train)
  h5f.create_dataset('X_test', data=x_test)
  h5f.create_dataset('y_train', data=y_train)
  h5f.create_dataset('y_test', data=y_test)
  h5f.close()
  
  h5f = h5py.File('test_hdf5.h5', 'r')
  x_train = h5f['X_train']
  x_test = h5f['X_test']
  y_train = h5f['y_train']
  y_test = h5f['y_test']
  
  # Build 3 layer DNN with 10, 20, 10 units respectively.
  feature_columns = learn.infer_real_valued_columns_from_input(x_train)
  classifier = learn.TensorFlowDNNClassifier(
      feature_columns=feature_columns, hidden_units=[10, 20, 10], n_classes=3,
      steps=200)
  
  # Fit and predict.
  classifier.fit(x_train, y_train)
  score = metrics.accuracy_score(y_test, classifier.predict(x_test))
  print('Accuracy: {0:f}'.format(score))
  
  