title: tensorflow difference between saving model via exporter and tf.train.write_graph()?
body: <p>What  is difference between saving a model by</p>
  
  <ol>
  <li>using exporter as specified in tensorflow serving:</li>
  </ol>
  
  <p>eg:</p>
  
  <pre><code>from tensorflow.contrib.session_bundle import exporter
  #from tensorflow_serving.session_bundle import exporter
  saver = tf.train.Saver(sharded=True)
  model_exporter = exporter.Exporter(saver)
  model_exporter.init(
          sess.graph.as_graph_def(),
          named_graph_signatures={
              'inputs': exporter.generic_signature({'images': x}),
              'outputs': exporter.generic_signature({'scores': y})})
  model_exporter.export(export_path, tf.constant(FLAGS.export_version), sess) 
  </code></pre>
  
  <ol start="2">
  <li>Using tf.train.write_graph() and tf.train.Saver() directly:</li>
  </ol>
  
  <p>eg:</p>
  
  <pre><code>with sess.graph.as_default():
      saver = tf.train.Saver()
      saver.save(sess, path, meta_graph_suffix='meta', write_meta_graph=True)
  </code></pre>
  
  <p>Question is in continuation of <a href="https://stackoverflow.com/questions/38947658/tensorflow-saving-into-loading-a-graph-from-a-file">TensorFlow saving into/loading a graph from a file</a></p>