title: TensorFlow "tf.estimator Quickstart" example doesn't produce the same output as the tutorial
body: <p>I just started practicing with TensorFlow and Python. I copied and executed the code in the <a href="https://www.tensorflow.org/get_started/estimator" rel="nofollow noreferrer">tf.estimator Quickstart</a> page of the TensorFlow website:</p>
  
  <pre><code>from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import os
  import urllib
  
  import numpy as np
  import tensorflow as tf
  
  # Data sets
  IRIS_TRAINING = "iris_training.csv"
  IRIS_TRAINING_URL = "http://download.tensorflow.org/data/iris_training.csv"
  
  IRIS_TEST = "iris_test.csv"
  IRIS_TEST_URL = "http://download.tensorflow.org/data/iris_test.csv"
  
  
  def main():
      # If the training and test sets aren't stored locally, download them.
      if not os.path.exists(IRIS_TRAINING):
          raw = urllib.urlopen(IRIS_TRAINING_URL).read()
          with open(IRIS_TRAINING, "w") as f:
              f.write(raw)
  
      if not os.path.exists(IRIS_TEST):
          raw = urllib.urlopen(IRIS_TEST_URL).read()
          with open(IRIS_TEST, "w") as f:
              f.write(raw)
  
      # Load datasets.
      training_set = tf.contrib.learn.datasets.base.load_csv_with_header(
          filename=IRIS_TRAINING,
          target_dtype=np.int,
          features_dtype=np.float32)
      test_set = tf.contrib.learn.datasets.base.load_csv_with_header(
          filename=IRIS_TEST,
          target_dtype=np.int,
          features_dtype=np.float32)
  
      # Specify that all features have real-value data
      feature_columns = [tf.feature_column.numeric_column("x", shape=[4])]
  
      # Build 3 layer DNN with 10, 20, 10 units respectively.
      classifier = tf.estimator.DNNClassifier(feature_columns=feature_columns,
                                              hidden_units=[10, 20, 10],
                                              n_classes=3,
                                              model_dir="/tmp/iris_model")
      # Define the training inputs
      train_input_fn = tf.estimator.inputs.numpy_input_fn(
          x={"x": np.array(training_set.data)},
          y=np.array(training_set.target),
          num_epochs=None,
          shuffle=True)
  
      # Train model.
      classifier.train(input_fn=train_input_fn, steps=2000)
  
      # Define the test inputs
      test_input_fn = tf.estimator.inputs.numpy_input_fn(
          x={"x": np.array(test_set.data)},
          y=np.array(test_set.target),
          num_epochs=1,
          shuffle=False)
  
      # Evaluate accuracy.
      accuracy_score = classifier.evaluate(input_fn=test_input_fn)["accuracy"]
  
      print("\nTest Accuracy: {0:f}\n".format(accuracy_score))
  
      # Classify two new flower samples.
      new_samples = np.array(
          [[6.4, 3.2, 4.5, 1.5],
           [5.8, 3.1, 5.0, 1.7]], dtype=np.float32)
      predict_input_fn = tf.estimator.inputs.numpy_input_fn(
          x={"x": new_samples},
          num_epochs=1,
          shuffle=False)
  
      predictions = list(classifier.predict(input_fn=predict_input_fn))
      predicted_classes = [p["classes"] for p in predictions]
  
      print(
          "New Samples, Class Predictions:    {}\n"
              .format(predicted_classes))
  
  
  if __name__ == "__main__":
      main()
  </code></pre>
  
  <p>According to the tutorial, the output should be</p>
  
  <blockquote>
    <p>Test Accuracy: 0.966667</p>
    
    <p>New Samples, Class Predictions:    [1 2]</p>
  </blockquote>
  
  <p>My problem is that the output given by my execution of the source code is </p>
  
  <blockquote>
    <p>Test Accuracy: 0.966667</p>
    
    <p>New Samples, Class Predictions:  [array([b'1'], dtype=object), array([b'1'], dtype=object)]</p>
  </blockquote>
  
  <p>Unfortunately I'm not experienced enough with either TensorFlow or Python to understand what the problem is. The only guess i can make is some compatibility issue, but I installed Python 3.5 for Windows following the <a href="https://www.tensorflow.org/install/install_windows" rel="nofollow noreferrer">tutorial</a> on the TensorFlow website.</p>