If a model involves a lot of math, is it more helpful to be able to debug it? What tf lacks is an intuitive debugging tool. I think this is where pytorch excels.
You don't often need to debug, especially if the model can be checked on compile time. Think static types for programming. For Tensorflow all the data types and tensor dimensions are checked before loading any data, it the math is derived correctly then it is not necessary to even debug.
If data and model are mixed, it often resort to line-by-line debugging to zone out the real problem, which often takes more time.
If data and model are mixed, it often resort to line-by-line debugging to zone out the real problem, which often takes more time.