Why is tc_loss in bTCVAE negative? about disentangling-vae HOT 11 OPEN

def _get_log_pz_qz_prodzi_qzCx(latent_sample, latent_dist,n_data, is_mss=False):
    batch_size, hidden_dim = latent_sample.shape

    # calculate log q(z|x)
    log_q_zCx = log_density_gaussian(latent_sample, *latent_dist).sum(dim=1)

    # calculate log p(z)
    # mean and log var is 0
    zeros = torch.zeros_like(latent_sample)
    log_pz = log_density_gaussian(latent_sample, zeros, zeros).sum(1)

    if not self.is_mss:
        log_qz, log_prod_qzi = _minibatch_weighted_sampling(latent_dist,
                                                            latent_sample,
                                                            n_data)

    else:
        log_qz, log_prod_qzi = _minibatch_stratified_sampling(latent_dist,
                                                              latent_sample,
                                                              n_data)

    return log_pz, log_qz, log_prod_qzi, log_q_zCx


def _minibatch_weighted_sampling(latent_dist, latent_sample, data_size):
    """
    Estimates log q(z) and the log (product of marginals of q(z_j)) with minibatch
    weighted sampling.

    Parameters
    ----------
    latent_dist : tuple of torch.tensor
        sufficient statistics of the latent dimension. E.g. for gaussian
        (mean, log_var) each of shape : (batch_size, latent_dim).

    latent_sample: torch.Tensor
        sample from the latent dimension using the reparameterisation trick
        shape : (batch_size, latent_dim).

    data_size : int
        Number of data in the training set

    References 
    -----------
       [1] Chen, Tian Qi, et al. "Isolating sources of disentanglement in variational
       autoencoders." Advances in Neural Information Processing Systems. 2018.
    """
    batch_size = latent_sample.size(0)

    mat_log_qz = matrix_log_density_gaussian(latent_sample, *latent_dist)

    log_prod_qzi = (torch.logsumexp(mat_log_qz, dim=1, keepdim=False) -
                    math.log(batch_size * data_size)).sum(dim=1)
    log_qz = torch.logsumexp(mat_log_qz.sum(2), dim=1, keepdim=False
                             ) - math.log(batch_size * data_size)

    return log_qz, log_prod_qzi


def _minibatch_stratified_sampling(latent_dist, latent_sample, data_size):
    """
    Estimates log q(z) and the log (product of marginals of q(z_j)) with minibatch
    stratified sampling.
    
    Parameters
    -----------
    latent_dist : tuple of torch.tensor
        sufficient statistics of the latent dimension. E.g. for gaussian
        (mean, log_var) each of shape : (batch_size, latent_dim).

    latent_sample: torch.Tensor
        sample from the latent dimension using the reparameterisation trick
        shape : (batch_size, latent_dim).

    data_size : int
        Number of data in the training set

    References 
    -----------
       [1] Chen, Tian Qi, et al. "Isolating sources of disentanglement in variational
       autoencoders." Advances in Neural Information Processing Systems. 2018.
    """
    batch_size = latent_sample.size(0)

    mat_log_qz = matrix_log_density_gaussian(latent_sample, *latent_dist)

    log_iw_mat = log_importance_weight_matrix(batch_size, data_size).to(latent_sample.device)
    log_qz = torch.logsumexp(log_iw_mat + mat_log_qz.sum(2), dim=1, keepdim=False)
    log_prod_qzi = torch.logsumexp(log_iw_mat.view(batch_size, batch_size, 1) +
                                   mat_log_qz, dim=1, keepdim=False).sum(1)

    return log_qz, log_prod_qzi