Benchmarking Deep Neural Models

Benchmarking code Deep neural models can be considered as code snippets written by machines from data. Benchmarking traditional code considers metrics such as running time and memory usage. Deep models differ from traditional code when it comes to benchmarking for at least two reasons: Constant running time: Deep neural networks run in constant FLOPs, i.e. most networks consume a fixed number of operations, whereas some hand-coded algorithms are iterative and may run an unknown but bounded number of operations before reaching a result.