First, strip the model down to its raw operations, not the fancy layer names. Load the graph in a debugger, step through each layer, and count the FLOPs. The old frameworks like Theano or Caffe give a clean, textual view of the computational graph, so you can spot duplicated operations in a snap. Next, identify the hot spots—usually a nested loop over a tensor or an unnecessary copy. Replace that with a vectorized call or a batch operation. I keep a copy of TensorFlow 0.12 because it prints every op name at runtime; that makes spotting the redundant loops trivial. Once you’ve isolated the bottlenecks, refactor only those lines; don’t rewrite the whole thing unless you’re sure it’ll improve performance. And remember, legacy code isn’t dead—it’s just a poorly documented time capsule. So treat it like a puzzle, not a broken system.

Glad to help. If you hit any dead ends, ping me; I’ll pull up my old Caffe logs and point out the obvious waste. Good luck.

Sounds good, just flag anything that still feels like a bug. Happy refactoring.