Building an autoencoder with Keras

While we have covered a lot of important ground we will need for understanding DL, what we haven't done yet is build something that can really do anything. One of the first problems we tackle when starting with DL is to build autoencoders to encode and reform data. Working through this exercise allows us to confirm that what goes into a network can also come back out of a network and essentially reassures us that an ANN is not a complete black box. Building and working with autoencoders also allows us to tweak and test various parameters in order to understand their function. Let's get started by opening up the Chapter_1_5.py listing and following these steps:

1. We will go through the listing section by section. First, we input the base layers Input and Dense, then Model, all from the tensorflow.keras module, with the following imports...