01ed847eea06300d278ffcf1214021b487d3d463,mnist/distributed/trainer/model.py,Model,export,#Model#Any#Any#,129

Before Change

      sess.run(init_op)
      trained_saver = tf.train.Saver()
      trained_saver.restore(sess, last_checkpoint)
      saver = tf.train.Saver()
      saver.export_meta_graph(filename=os.path.join(output_dir, "export.meta"))
      saver.save(
          sess, os.path.join(output_dir, "export"), write_meta_graph=False)

  def build_prediction_graph(self):
    Builds prediction graph and registers appropriate endpoints.
    examples = tf.placeholder(tf.string, shape=(None,))

After Change

    logging.info("Exporting prediction graph to %s", output_dir)
    with tf.Session(graph=tf.Graph()) as sess:
      // Build and save prediction meta graph and trained variable values.
      input_signatures, output_signatures = self.build_prediction_graph()
      // Remove this if once Tensorflow 0.12 is standard.
      try:
        init_op = tf.global_variables_initializer()
      except AttributeError:
        init_op = tf.initialize_all_variables()
      sess.run(init_op)
      trained_saver = tf.train.Saver()
      trained_saver.restore(sess, last_checkpoint)

      predict_signature_def = signature_def_utils.build_signature_def(
          input_signatures, output_signatures,
          signature_constants.PREDICT_METHOD_NAME)
      // Create a saver for writing SavedModel training checkpoints.
      build = builder.SavedModelBuilder(
          os.path.join(output_dir, "saved_model"))
      build.add_meta_graph_and_variables(
          sess, [tag_constants.SERVING],
          signature_def_map={
              signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
                  predict_signature_def
          },
          assets_collection=tf.get_collection(tf.GraphKeys.ASSET_FILEPATHS))
      build.save()

  def build_prediction_graph(self):
    Builds prediction graph and registers appropriate endpoints.
    examples = tf.placeholder(tf.string, shape=(None,))

Instances