Simple functions for saving a TensorFlow graph as a SavedModel, followed by loading it again in TensorFlow 1.13.

tf_1.13_saved_model_save_and_load.py

def use_tf_saved_model(X, y, sess):
    builder = tf.saved_model.builder.SavedModelBuilder('./data/save_model')
    builder.add_meta_graph_and_variables(
        sess,
        [tf.saved_model.tag_constants.SERVING],
        signature_def_map={
            tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY: tf.saved_model.predict_signature_def(
                inputs={'X': X},
                outputs={'y': y}
            )
        },
        strip_default_attrs=True)
    builder.save()

def load_tf_saved_model(sess, in_name, out_name):
    tf.saved_model.load(sess, [tf.saved_model.tag_constants.SERVING], './data/save_model')
    graph = tf.get_default_graph()
    X = graph.get_tensor_by_name(in_name)
    y = graph.get_tensor_by_name(out_name)
    return X, y