TensorFlow Estimators

So far, in this chapter, we have mostly focused on the low-level TensorFlow API. We used decorators to modify functions to compile the computational graphs explicitly for computational efficiency. Then, we worked with the Keras API and implemented feedforward NNs, to which we added customized layers. In this section, we will switch gears and work with TensorFlow Estimators. The tf.estimator API encapsulates the underlying steps in machine learning tasks, such as training, prediction (inference), and evaluation. Estimators are more encapsulated but also more scalable when compared to the previous approaches that we have covered in this chapter. Also, the tf.estimator API adds support for running models on multiple platforms without requiring major code changes, which makes them more suitable for the so-called "production phase" in industry applications. In addition, TensorFlow comes with a selection of off-the-shelf estimators for common machine learning...