Does tensorflow use automatic or symbolic gradients?

Question

I haven\'t been able to find a clear statement of whether tensorflow uses automatic or symbolic differentiation.

I skimmed the tensorflow paper and they mention auto

Answer 1

Afaik symbolic differentiation means working with a mathematical, symbolic equation (i.e., symbolic math equation in, derivative of the equation out). Automatic differentiation computes derivatives based on computational functions (which in turn are broken down into basic operations such as addition/subtraction and multipliation/division).

Since TensorFlow does differentiation based on a computation graph of operations, I'd intuitively say that it's automatic differentiation (I don't know of any other technique that would be appropriate here; I think the possibility that TensorFlow is converting the computation graph into a mathematical equation that is then parsed to compute the derivative of that equation is prob. out of question). The authors say "symbolic differentiation" in the TensorFlow whitepaper though -- however, I think this may be a misnomer similar to "Tensor" instead of "(multi-dimensional) data array" if you'd ask a mathematician.