Implementing autoencoders with TensorFlow 2
In this section, we'll illustrate how to implement several of the autoencoder models introduced in the previous section using the Keras interface of TensorFlow 2. We'll first load and prepare an image dataset that we'll use throughout this section. We will use images instead of financial time series because it makes it easier to visualize the results of the encoding process. The next section shows how to use an autoencoder with financial data as part of a more complex architecture that can serve as the basis for a trading strategy.
After preparing the data, we'll proceed to build autoencoders using deep feedforward nets, sparsity constraints, and convolutions and apply the latter to denoise images.
How to prepare the data
For illustration, we'll use the Fashion MNIST dataset, a modern drop-in replacement for the classic MNIST handwritten digit dataset popularized by Lecun et al. (1998) with LeNet. We...
