Fig. 3.

Example code for training a deep Boltzmann machine and using it as a generative model. First, the user needs to log in to the Opal server, where the data is stored. If the specified data set is available, and the user has the correct access rights, the data set is loaded into the R session. The loaded data can be split into training and test data before the training. In the subsequent call to the fitting function, which by default also collects monitoring data from the training, the most important parameters for training a DBM are included. The numbers of hidden nodes for each of the hidden layers (“nhiddens”) determine the model architecture. The learning rate and the number of epochs for pre-training and fine-tuning of the DBM are the most important parameters for the optimisation procedure. If a good solution has been found, the model can be used to generate synthetic data and return it to the client