Table 2 Overview of client-side functions for training and using DBM models
Function name | Short description |
|---|---|
ds.monitored_fitrbm | Monitored training of an RBM model |
ds.monitored_stackrbms | Monitored training of a stack of RBMs. Can be used for pre-training a DBM or for training a DBN |
ds.monitored_fitdbm | Monitored training of a DBM, including pre-training and fine-tuning |
ds.setJuliaSeed | Set a seed for the random number generator |
ds.dbm.samples/ ds.rbm.samples | Generate samples from a DBM/RBM. This also allows conditional sampling. |
ds.bm.defineLayer | Define training parameters individually for a RBM layer in a DBM or DBN |
ds.bm.definePartitionedLayer | Define a partitioned layer using other layers as parts |
ds.dbm.top2LatentDims | Get a two-dimensional representation of latent features |
ds.rbm.loglikelihood | Estimates the partition function of an RBM with AIS and then calculates the log-likelihood |
ds.dbm.loglikelihood | Performs a separate AIS run for each of the samples to estimate the log-likelihood of a DBM |
ds.dbm.logproblowerbound | Estimates the variational lower bound of the likelihood of a DBM with AIS |
ds.rbm.exactloglikelihood/ ds.dbm.exactloglikelihood | Calculates the log-likelihood for a RBM/DBM (exponential complexity) |