gdir.py 文件源码

def e_step(self, epsilon, q, y, *params):
        model        = self.model
        prior_params = model.get_prior_params(*params)
        h            = model.prior.step_sample(epsilon, q)
        py           = model.p_y_given_h(h, *params)

        consider_constant = [y] + list(params)

        log_py_h = -model.conditional.neg_log_prob(y[None, :, :], py)
        if model.prior.has_kl:
            KL_q_p = model.prior.step_kl_divergence(q, *prior_params)
        else:
            log_ph = -model.prior.neg_log_prob(h)
            log_qh = -model.posterior.neg_log_prob(h, q[None, :, :])
            KL_q_p = (log_qh - log_ph).mean(axis=0)
        y_energy = -log_py_h.mean(axis=0)

        cost = (y_energy + KL_q_p).mean(axis=0)
        grad = theano.grad(cost, wrt=q, consider_constant=consider_constant)

        cost = y_energy.mean()
        return cost, grad