train_tnnmlTreeNeuralNetwork.train_tnn : schedule -> tnn -> tnnex * tnnex -> tnnTrain a tree neural network (TNN) on a set of examples via backpropagation to minimize mean square error.
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).
Fails when dimension constraints are not respected (see
mlTreeNeuralNetwork.random_tnn) or a variable/constant from
the examples is not defined in the TNN.
See the end of the file
src/AI/machine_learning/mlTreeNeuralNetwork.sml for a toy
example.