You can get a direct reference to the graphs if you want, that will let you do anything tensorflow lets you do. I think this is what you want:

  # This assumes your model is ready to be called with .predict()
  sess = keras.get_session()
  graph = sess.graph
  graph_dev = graph.as_graph_def()

  frozen_graph = tf.graph_util.convert_variables_to_constants(
      sess, graph_def, nodes_to_output)

  encoded_frozen_graph = frozen_graph.SerializeToString()