Using Keras as a TensorFlow Interface
We are using Keras because it simplifies the TensorFlow interface into general abstractions and, in TensorFlow 2.0, this is the default API in this version. In the backend, the computations are still performed in TensorFlow, but we spend less time worrying about individual components, such as variables and operations, and spend more time building the network as a computational unit. Keras makes it easy to experiment with different architectures and hyperparameters, moving more quickly toward a performant solution.
As of TensorFlow 2.0.0, Keras is now officially distributed with TensorFlow as tf.keras
. This suggests that Keras is now tightly integrated with TensorFlow and will likely continue to be developed as an open source tool for a long period of time. Components are an integral part when building models. Let's deep dive into this concept now.
Model Components
As we saw in Chapter 1, Introduction to Neural Networks and Deep Learning...