TensorFlow requires the user to maintain a dataflow-graph, as if the user is writing a compiler, which IMHO is silly. It's the opposite of convenience.
What TensorFlow should do instead: do dataflow-analysis, like any modern compiler, and figure out the dataflow-graphs at compile-time.
OR... take PyTorch's approach and use dynamic graphs. I bet there's not even a significant performance penalty associated with dynamic graphs, as the tensors are usually quite large and consume most of the computation-time anyway.
My point: why use a tool that's founded on a wrong design-decision?
What TensorFlow should do instead: do dataflow-analysis, like any modern compiler, and figure out the dataflow-graphs at compile-time.
OR... take PyTorch's approach and use dynamic graphs. I bet there's not even a significant performance penalty associated with dynamic graphs, as the tensors are usually quite large and consume most of the computation-time anyway.
My point: why use a tool that's founded on a wrong design-decision?