How to prune and optimize an existing saved model and it variables in tensorflow python?

I have a existing tensorflow saved model with the directory structure below. That is basically the only thing I have. I can manage to get the checkpoint files though, but it would be great if we don’t need anything from training procedure.


Now I would like to run the to optimize it for later inference in tf serving.

However, even though I can successfully optimize its graph_def, I cannot prepare appropriate variables for the new graph. The problem reside in the Save and Restore op, which exists in the old graph, but are pruned in the optimized graph_def.

But the variables file contains the save/restore op, it will give errors like

Save/xxx does not exist in graph.

I tried to leverage the graph_util.convert_variables_to_constant function, but I got the

Attempting to use unitialized node xxx/yyy

How can I prune the graph structure and make successfull predictions? Thanks.