variational_inference_test.py 文件源码

def testDefaultsSampleKLWithoutAnalyticKLOrEntropy(self):
    x = constant_op.constant([[-6., 3., 6.]])

    prior = distributions.Bernoulli(0.5)
    variational = st.StochasticTensor(
        NormalNoEntropy(
            loc=inference_net(x, 1), scale=1.))
    vi.register_prior(variational, prior)
    px = distributions.Normal(loc=generative_net(variational, 3), scale=1.)
    log_likelihood = math_ops.reduce_sum(px.log_prob(x), 1)

    # No analytic KL available between prior and variational distributions.
    with self.assertRaisesRegexp(NotImplementedError, "No KL"):
      distributions.kl(variational.distribution, prior)

    elbo = vi.elbo(
        variational_with_prior={variational: prior},
        log_likelihood=log_likelihood)
    expected_elbo = log_likelihood + prior.log_prob(
        variational) - variational.distribution.log_prob(variational)

    with self.test_session() as sess:
      sess.run(variables.global_variables_initializer())
      self.assertAllEqual(*sess.run([expected_elbo, elbo]))