Update tester.py

    def loss_fn(self, original_seq, recon_seq_mu, recon_seq_logvar, s_mean, s_logvar, d_post_mean, d_post_logvar, d_prior_mean, d_prior_logvar):

        batch_size = original_seq.size(0)

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

        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))

        # See https://arxiv.org/pdf/1606.05908.pdf, Page 9, Section 2.2, Equation (7) for details.

        kld_s = -0.5 * torch.sum(1 + s_logvar - torch.pow(s_mean, 2) - torch.exp(s_logvar))

        # See https://arxiv.org/pdf/1606.05908.pdf, Page 9, Section 2.2, Equation (6) for details.

        d_post_var = torch.exp(d_post_logvar)

        d_prior_var = torch.exp(d_prior_logvar)

        kld_d = 0.5 * torch.sum(d_prior_logvar - d_post_logvar + 

    def loglikelihood_last_timestamp(self, x, recon_x_mu, recon_x_logvar):

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

        llh = -0.5 * torch.sum(torch.pow(((x.float()-recon_x_mu.float())/torch.exp(recon_x_logvar.float())), 2) 

              + 2 * recon_x_logvar.float() 

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