stochastic_graph_test.py 文件源码

def testPathwiseDerivativeDoesNotAddSurrogateLosses(self):
    with self.test_session():
      mu = [0.0, 0.1, 0.2]
      sigma = constant_op.constant([1.1, 1.2, 1.3])
      with st.value_type(st.SampleValue()):
        prior = st.StochasticTensor(distributions.Normal(loc=mu, scale=sigma))
        likelihood = st.StochasticTensor(
            distributions.Normal(
                loc=prior, scale=sigma))
        self.assertEqual(
            prior.distribution.reparameterization_type,
            distributions.FULLY_REPARAMETERIZED)
        self.assertEqual(
            likelihood.distribution.reparameterization_type,
            distributions.FULLY_REPARAMETERIZED)

      loss = math_ops.square(array_ops.identity(likelihood) - [0.0, 0.1, 0.2])
      sum_loss = math_ops.reduce_sum(loss)

      surrogate_loss = sg.surrogate_loss([loss])
      with self.assertRaisesRegexp(ValueError, "dimensionality 1 or greater"):
        _ = sg.surrogate_loss([sum_loss])
      surrogate_from_both = sg.surrogate_loss(
          [loss, sum_loss * array_ops.ones_like(loss)])

      # Pathwise derivative terms do not require add'l surrogate loss terms.
      with self.test_session() as sess:
        self.assertAllClose(*sess.run([loss, surrogate_loss]))
        self.assertAllClose(*sess.run([(loss + sum_loss), surrogate_from_both]))