In this chapter, we analyzed how TensorFlow works under the hood—the separation between the graph definition phase and its execution within a session, how to use the Python API to interact with a graph, and how to define a model and measure the metrics during training.
It's worth noting that this chapter analyzed how TensorFlow works in its static graph version, which is no longer the default in TensorFlow 2.0; however, the graph is still present and even when used in eager mode, every API call produces operations that can be executed inside a graph to speed up execution. As will be shown in the next chapter, TensorFlow 2.0 still allows models to be defined in static graph mode, especially when defining models using the Estimator API.
Having knowledge of graph representation is of fundamental importance, and having at least an intuitive idea about the advantages...