Update trainer.py

        # log(N(x|mu,sigma^2))

        # = log{1/(sqrt(2*pi)*sigma)exp{-(x-mu)^2/(2*sigma^2)}} 

        # = -0.5*{log(2*pi)+2*log(sigma)+[(x-mu)/exp{log(sigma)}]^2}

        # Note that var = sigma^2, so “recon_seq_logvar” is more appropriate to be called “recon_seq_logsigma”, but the name does not the matter

        # Note that var = sigma^2, i.e., log(var) = 2*log(sigma),

        # so here the “recon_seq_logvar” is more appropriate to be called “recon_seq_logsigma”, but the name does not the matter

        loglikelihood = -0.5 * torch.sum(torch.pow(((original_seq.float()-recon_seq_mu.float())/torch.exp(recon_seq_logvar.float())), 2) 

                                         + 2 * recon_seq_logvar.float() 

                                         + np.log(np.pi*2))