KLD in Loss function

[gissemari]

Hi, would you tell me why the kld calculation only involves what it seems to be the encoder or approximated (to the posterior) function? Isn't it calculated base on 2 distributions?

        mu = self.context_to_mu(context)
        logvar = self.context_to_logvar(context) # to z sampled from
        std = t.exp(0.5 * logvar)
        z = Variable(t.randn([batch_size, self.params.latent_variable_size]))
        if use_cuda:
            z = z.cuda()
        z = z * std + mu
        kld = (-0.5 * t.sum(logvar - t.pow(mu, 2) - t.exp(logvar) + 1, 1)).mean().squeeze()