I want to compare the time complexity of two deep neural networks, but I have no idea how to go about it. How do I graphically achieve that with respect to the number of iterations, accuracy or any other metric?


Time complexity in Big-O doesn't make much sense as it is dominated by matrix multiplication.

There are three things you can do:

  • Calculate the operations necessary for one training step
  • Measure time one training step (average!!!)
  • Measure time for training until a certain accuracy / error / score is reached

The last one is practically the most relevant.

Don't forget to mention hardware (could+GPU) and software (drivers, cuda, cudnn; all with versions) being used - that makes a huge difference

See tables in the appendix of https://arxiv.org/abs/1707.09725 to get some "design performance numbers" of famous architecture


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