You can get all of the node names in your model with something like:
node_names = [node.name for node in tf.get_default_graph().as_graph_def().node]
Or with restoring the graph:
saver = tf.train.import_meta_graph(/path/to/meta/graph)
sess = tf.Session()
saver.restore(sess, /path/to/checkpoints)
graph = sess.graph
print([node.name for node in graph.as_graph_def().node])
You may need to filter these to get only your output nodes, or only the nodes that you want, but this can at least help you get the names for a graph that you have already trained and cannot afford to retrain with name='some_name' defined for each node.
Ideally, you want to define a name parameter for each operation or tensor that you are going to want to access later.