Deep Learning · Module 10
Answers and explanations · Turn a successful run into a reproducible experiment
Compare these references after your own attempt. You can read them at any time; they do not record a live model or vendor run.
Deep Learning · Module 10
Compare these references after your own attempt. You can read them at any time; they do not record a live model or vendor run.
10A. Missing pieces include architecture, feature order, data provenance and split IDs, preprocessing state, loss/optimizer/settings, selection rule, seed/runtime versions and prior test exposure. Optimizer state is additionally needed for exact training resumption. A score alone cannot identify the experiment.
10B. State dictionaries can contain tensor references connected to the live model; later updates can change what you thought was a frozen best state. The implementation uses copy.deepcopy(model.state_dict()) at improvement, then reloads it after training.
10C. Test sample remains 120. Repeating verifies deterministic execution under the recorded environment, not independent sampling or improved statistical certainty.