In this section, we look at how a DBN is pretrained:
class RBM(UnsupervisedModel):... def pretrain(self, train_set, validation_set=None): """Perform Unsupervised pretraining of the DBN.""" self.do_pretrain = True def set_params_func(rbmmachine, rbmgraph): params = rbmmachine.get_parameters(graph=rbmgraph) self.encoding_w_.append(params['W']) self.encoding_b_.append(params['bh_']) return SupervisedModel.pretrain_procedure( self, self.rbms, self.rbm_graphs, set_params_func=set_params_func, train_set=train_set, validation_set=validation_set)
This in turn calls SupervisedModel.pretrain_procedure(..), which takes the following parameters:
- layer_objs: A list of model objects (autoencoders or RBMs)
- layer_graphs: A list of model ...
