Encoding and decoding layers

The autoencoder can be broken down into two major components that serve specific purposes during an unsupervised learning process. The left side of Figure 7.2 shows an autoencoder that is implemented using fully connected (dense) layers. It receives as input some vector and then it goes into six hidden layers; the first three, with 6, 4, and 2 neurons, respectively, are meant to compress the input data down to two dimensions, since the output of two neurons is two scalar values. This first set of layers is known as the encoder:

Figure 7.2 – Two representations of an autoencoder. Left: full and descriptive model. Right: compact and abstracted model representation

The second set of neurons is meant to reconstruct the input data back to its original dimensionality and values using three layers with 4, 6, and 8 neurons, respectively; this group of layers is known as the decoder.

Note that the last layer of the autoencoder must have the same number of...