274ec230054107d30bb91de43ec42b7393170928,qucumber/utils/training_statistics.py,,NLL,#,97

Before Change

    Z = nn_state.compute_normalization(space)
    eps = 0.000001
    if train_bases is None:
        for i in range(len(samples)):
            NLL -= (cplx.norm_sqr(nn_state.psi(samples[i])) + eps).log()
            NLL += Z.log()
    else:

After Change

                    if samples[i, nn_state.num_visible - j - 1] == 1:
                        ind += pow(2, j)
                probs_r = cplx.norm_sqr(psi_r[:, ind]) / Z
                NLL -= probs_to_logits(probs_r).item()
    return (NLL / float(len(samples))).item()


def KL(nn_state, target_psi, space, bases=None, **kwargs):

Instances