What are Symbolic and Imperative APIs in TensorFlow 2.0?

One of my favorite things about TensorFlow 2.0 is that it offers multiple levels of abstraction, so you can choose the right one for your project. In this article, I'll explain the tradeoffs between two styles you can use to create your neural networks. The first is a symbolic style, in which you build a model by manipulating a graph of layers. The second is an imperative style, in which you build a model by extending a class. I'll introduce these, share notes on important design and usability considerations, and close with quick recommendations to help you choose the right one.