def pretrain(self, train_set, validation_set=None): self.do_pretrain = True def set_params_func(rbmmachine, rbmgraph): params = rbmmachine.get_model_parameters(graph=rbmgraph) self.encoding_w_.append(params['W']) self.encoding_b_.append(params['bh_']) return UnsupervisedModel.pretrain_procedure(self, self.rbms, self.rbm_graphs, set_params_func=set_params_func, train_set=train_set, validation_set=validation_set)
def pretrain(self, train_set, validation_set=None): self.do_pretrain = True def set_params_func(autoenc, autoencgraph): params = autoenc.get_model_parameters(graph=autoencgraph) self.encoding_w_.append(params['enc_w']) self.encoding_b_.append(params['enc_b']) return UnsupervisedModel.pretrain_procedure(self, self.autoencoders, self.autoencoder_graphs, set_params_func=set_params_func, train_set=train_set, validation_set=validation_set)
def pretrain(self, train_set, validation_set=None): self.do_pretrain = True def set_params_func(rbmmachine, rbmgraph): params = rbmmachine.get_model_parameters(graph=rbmgraph) self.encoding_w_.append(params['W']) self.encoding_b_.append(params['bh_']) return UnsupervisedModel.pretrain_procedure( self, self.rbms, self.rbm_graphs, set_params_func=set_params_func, train_set=train_set, validation_set=validation_set)
def pretrain(self, train_set, validation_set=None): """Perform Unsupervised pretraining of the autoencoder.""" self.do_pretrain = True def set_params_func(rbmmachine, rbmgraph): params = rbmmachine.get_model_parameters(graph=rbmgraph) self.encoding_w_.append(params["W"]) self.encoding_b_.append(params["bh_"]) return UnsupervisedModel.pretrain_procedure( self, self.rbms, self.rbm_graphs, set_params_func=set_params_func, train_set=train_set, validation_set=validation_set, )