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Autoencoders
This results in producing a bottleneck effect in the flow of information in
the network. The hidden layer in between is also called the "bottleneck layer."
Learning in the autoencoder consists of developing a compact representation of
the input signal at the hidden layer so that the output layer can faithfully reproduce
the original input.
In the following diagram, you can see the architecture of Vanilla autoencoder:
Figure 2: Architecture of the Vanilla autoencoder, visualized
Let us try to build a Vanilla autoencoder. While in the paper Hinton used it for
dimension reduction, in the code to follow we will use autoencoders for image
reconstruction. We will train the autoencoder on the MNIST database and will use it
to reconstruct the test images. In the code, we will use the TensorFlow Keras Layers
class to build our own encoder and decoder layers, so firstly let's learn a little about
the Layers class.
TensorFlow Keras layers ‒ defining custom
layers
TensorFlow provides an easy way to define your own custom layer both from
scratch or as a composition of existing layers. The TensorFlow Keras layers package
defines a Layers object. We can make our own layer by simply making it a child
class of the Layers class. It is necessary to define the dimensions of the output while
defining the layer. Though input dimensions are optional, if you do not define them,
it will infer it automatically from the data. To build our own layer we will need to
implement three methods:
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