Simple reverse-mode Autodiff in Julia - Computational Chain

[@liangli-wei9316]

Man this is great. Thanks!

[@MachineLearningSimulation]

Thanks a lot, you're welcome.

[@kefre10]

One time you evaluated the derivate at 1.0 instead of 2.0, why did you get the same result?

[@kefre10]

Ah it needs the cotangend at this point

[@MachineLearningSimulation]

Great question! 😊 Yes, you figured it out. The pullback at lines 57 and 61 give the derivative if evaluated at 1.0. Since they effectively evaluate the vector-Jacobian product we need to provide a vector (here just a scalar) to left-multiply the Jacobian with. Using just 1.0 gives the unscaled derivative.