Update trainer.py

        # See https://arxiv.org/pdf/1606.05908.pdf, Page 9, Section 2.2 for details.

        # The constant items in the loss function (not the coefficients) can be any number here, or even omitted        

        # due to they have no any impact on gradientis propagation during training. So do in testing.

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

        # log(N(x|mean,var)) = 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, i.e., log(var) = 2*log(sigma),

        # so the “recon_seq_logvar” here 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)

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

                                         + 2 * recon_seq_logsigma.float() 

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