274ec230054107d30bb91de43ec42b7393170928,qucumber/utils/training_statistics.py,,NLL,#Any#Any#Any#Any#,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:
        for i in range(len(samples)):
            // Check whether the sample was measured the reference basis
            is_reference_basis = True

After Change

        nn_probs = nn_state.probability(samples, Z)
        NLL = torch.sum(probs_to_logits(nn_probs))
    else:
        unitary_dict = nn_state.unitary_dict
        // print(train_bases)
        for i in range(len(samples)):
            // Check whether the sample was measured the reference basis
            is_reference_basis = True
            for j in range(nn_state.num_visible):
                if train_bases[i][j] != "Z":
                    is_reference_basis = False
                    break
            if is_reference_basis is True:
                nn_probs = nn_state.probability(samples[i], Z)
                NLL += torch.sum(probs_to_logits(nn_probs))
            else:
                psi_r = rotate_psi(nn_state, train_bases[i], space, unitary_dict)
                // Get the index value of the sample state
                ind = 0
                for j in range(nn_state.num_visible):
                    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