Table 1 Most important hyperparameters for fitting a DBM. These parameters can be specified in the function “ds.monitored_fitdbm” (see Table 2). The parameters for pre-training can also be controlled individually for each layer (i.e. for each RBM in the stack) via the function “ds.bm.defineLayer”. Together with the function “ds.bm.definePartitionedLayer”, this allows to also create models with partitioned architectures
Hyperparameter name | Meaning of hyperparameter |
|---|---|
learningrate | Learning rate for stochastic gradient descent optimization |
learningratepretraining | Learning rate for pre-training, may be specified separately |
epochs | Number of training epochs |
epochspretraining | Number of epochs for pre-training, may be specified separately |
nhiddens | Number of hidden nodes specified as a vector of numbers, containing one number for each hidden layer |
batchsizepretraining | Batch size used in pre-training |