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, hdf5 format."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import numpy as np
  from sklearn import datasets
  from sklearn import metrics
  from sklearn import model_selection
  import tensorflow as tf
  import h5py  # pylint: disable=g-bad-import-order
  
  
  X_FEATURE = 'x'  # Name of the input feature.
  
  
  def main(unused_argv):
    # Load dataset.
    iris = datasets.load_iris()
    x_train, x_test, y_train, y_test = model_selection.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('/tmp/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('/tmp/test_hdf5.h5', 'r')
    x_train = np.array(h5f['X_train'])
    x_test = np.array(h5f['X_test'])
    y_train = np.array(h5f['y_train'])
    y_test = np.array(h5f['y_test'])
  
    # Build 3 layer DNN with 10, 20, 10 units respectively.
    feature_columns = [
        tf.feature_column.numeric_column(
            X_FEATURE, shape=np.array(x_train).shape[1:])]
    classifier = tf.estimator.DNNClassifier(
        feature_columns=feature_columns, hidden_units=[10, 20, 10], n_classes=3)
  
    # Train.
    train_input_fn = tf.estimator.inputs.numpy_input_fn(
        x={X_FEATURE: x_train}, y=y_train, num_epochs=None, shuffle=True)
    classifier.train(input_fn=train_input_fn, steps=200)
  
    # Predict.
    test_input_fn = tf.estimator.inputs.numpy_input_fn(
        x={X_FEATURE: x_test}, y=y_test, num_epochs=1, shuffle=False)
    predictions = classifier.predict(input_fn=test_input_fn)
    y_predicted = np.array(list(p['class_ids'] for p in predictions))
    y_predicted = y_predicted.reshape(np.array(y_test).shape)
  
    # Score with sklearn.
    score = metrics.accuracy_score(y_test, y_predicted)
    print('Accuracy (sklearn): {0:f}'.format(score))
  
    # Score with tensorflow.
    scores = classifier.evaluate(input_fn=test_input_fn)
    print('Accuracy (tensorflow): {0:f}'.format(scores['accuracy']))
  
  
  if __name__ == '__main__':
    tf.app.run()
  