Hyperparameters such as batch size, learning rate and number of epochs can be set
in the schedule arguments. The initial TNN can be constructed by calling
mlTreeNeuralNetwork.random_tnn. Examples consists of a term t and a list l.
The term t is expected to be lambda-free with each operator appearing with a
unique arity. The list l is expected to be a list of real numbers between 0 and
1. In the case of a simple objective each example (t,l) is to be written as
[(h(t),l)] where h is a variable representing the head network. For multiple
objectives, one can write [(h1(t),l1),...,(hn(t),ln)] for a single example.
The created list of examples is to be split into a training set and a test set (possibly empty).