title: tensorflow process gets killed
body: <p>I am trying to generate the pool 3 layer tensor value of some images. I tried with this code. I works fine for 3 to 4 image but when number of image is increased the process gets Killed after some time. </p>
  
  <pre><code>from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os.path
  import re
  import sys
  import tarfile
  from glob import glob
  from scipy import spatial
  
  import numpy as np
  from six.moves import urllib
  import tensorflow as tf
  
  FLAGS = tf.app.flags.FLAGS
  
  tf.app.flags.DEFINE_string(
      'model_dir', '/tmp/imagenet',
      """Path to classify_image_graph_def.pb, """
      """imagenet_synset_to_human_label_map.txt, and """
      """imagenet_2012_challenge_label_map_proto.pbtxt.""")
  tf.app.flags.DEFINE_string('image_file', '',
                             """Absolute path to image file.""")
  tf.app.flags.DEFINE_integer('num_top_predictions', 5,
                              """Display this many predictions.""")
  
  
  DATA_URL = 'http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz'
  
  class NodeLookup(object):
  
    def __init__(self,label_lookup_path=None,uid_lookup_path=None):
      if not label_lookup_path:
        label_lookup_path = os.path.join(
            FLAGS.model_dir, 'imagenet_2012_challenge_label_map_proto.pbtxt')
      if not uid_lookup_path:
        uid_lookup_path = os.path.join(
            FLAGS.model_dir, 'imagenet_synset_to_human_label_map.txt')
      self.node_lookup = self.load(label_lookup_path, uid_lookup_path)
  
    def load(self, label_lookup_path, uid_lookup_path):
  
      if not tf.gfile.Exists(uid_lookup_path):
        tf.logging.fatal('File does not exist %s', uid_lookup_path)
      if not tf.gfile.Exists(label_lookup_path):
        tf.logging.fatal('File does not exist %s', label_lookup_path)
  
      proto_as_ascii_lines = tf.gfile.GFile(uid_lookup_path).readlines()
      uid_to_human = {}
      p = re.compile(r'[n\d]*[ \S,]*')
      for line in proto_as_ascii_lines:
        parsed_items = p.findall(line)
        uid = parsed_items[0]
        human_string = parsed_items[2]
        uid_to_human[uid] = human_string
  
      node_id_to_uid = {}
      proto_as_ascii = tf.gfile.GFile(label_lookup_path).readlines()
      for line in proto_as_ascii:
        if line.startswith('  target_class:'):
          target_class = int(line.split(': ')[1])
        if line.startswith('  target_class_string:'):
          target_class_string = line.split(': ')[1]
          node_id_to_uid[target_class] = target_class_string[1:-2]
  
      node_id_to_name = {}
      for key, val in node_id_to_uid.items():
        if val not in uid_to_human:
          tf.logging.fatal('Failed to locate: %s', val)
        name = uid_to_human[val]
        node_id_to_name[key] = name
  
      return node_id_to_name
  
    def id_to_string(self, node_id):
      if node_id not in self.node_lookup:
        return ''
      return self.node_lookup[node_id]
  
  
  def create_graph():
  
    with tf.gfile.FastGFile(os.path.join(
        FLAGS.model_dir, 'classify_image_graph_def.pb'), 'rb') as f:
      graph_def = tf.GraphDef()
      graph_def.ParseFromString(f.read())
      _ = tf.import_graph_def(graph_def, name='')
  
  
  def run_inference_on_image(image):
  
    if not tf.gfile.Exists(image):
      tf.logging.fatal('File does not exist %s', image)
    image_data = tf.gfile.FastGFile(image, 'rb').read()
  
    # Creates graph from saved GraphDef.
    create_graph()
  
    with tf.Session() as sess:
      softmax_tensor = sess.graph.get_tensor_by_name('softmax:0')
      feature_tensor = sess.graph.get_tensor_by_name('pool_3:0')
      predictions = sess.run(softmax_tensor,
                             {'DecodeJpeg/contents:0': image_data})
      predictions = np.squeeze(predictions)
      feature_set = sess.run(feature_tensor,
                          {'DecodeJpeg/contents:0': image_data}) #ADDED
      feature_set = np.squeeze(feature_set) #ADDED
      return feature_set
  
      node_lookup = NodeLookup()
  
      top_k = predictions.argsort()[-FLAGS.num_top_predictions:][::-1]
      for node_id in top_k:
        human_string = node_lookup.id_to_string(node_id)
        score = predictions[node_id]
  
  def maybe_download_and_extract():
    dest_directory = FLAGS.model_dir
    if not os.path.exists(dest_directory):
      os.makedirs(dest_directory)
    filename = DATA_URL.split('/')[-1]
    filepath = os.path.join(dest_directory, filename)
    if not os.path.exists(filepath):
      def _progress(count, block_size, total_size):
        sys.stdout.write('\r&gt;&gt; Downloading %s %.1f%%' % (
            filename, float(count * block_size) / float(total_size) * 100.0))
        sys.stdout.flush()
      filepath, _ = urllib.request.urlretrieve(DATA_URL, filepath, _progress)
      print()
      statinfo = os.stat(filepath)
      print('Succesfully downloaded', filename, statinfo.st_size, 'bytes.')
    tarfile.open(filepath, 'r:gz').extractall(dest_directory)
  
  def main(_):
    maybe_download_and_extract()
    files = sorted(glob('*.jpg'))
    features = []
    result = []
    images = []
    i =0
    for image in files:
  
      images.append(image)
      run_inference_on_image(image)
      features += [run_inference_on_image(image)]
      new_result = 1 - spatial.distance.cosine(features[i], features[0])
      result.append(new_result)
      i +=1
    print (result)
    print (images)
  
  if __name__ == '__main__':
    tf.app.run()
  </code></pre>
  
  <p>It shows the error message before killing the process as </p>
  
  <pre><code>W tensorflow/core/framework/op_util.cc:332] op BatchNormWithGloabalNormalization is deprecated. It will cease to work in Graphdef version 9. Use tf.nn.batch_normalization().
  </code></pre>