def test_sgvb(self):
with self.get_session():
a = StochasticTensor(Normal(0., 1.),
observed=np.asarray([[0., 1., 2.]]))
b = StochasticTensor(Normal(1., 2.),
observed=np.asarray([[1., 2., 3.]]))
c = StochasticTensor(Normal(2., 3.),
observed=np.asarray([[2., 3., 4.]]))
lower_bound = sgvb([a, b], [c])
self.assertEqual(lower_bound.get_shape().as_list(), [1, 3])
np.testing.assert_almost_equal(lower_bound.eval(),
(a.log_prob() + b.log_prob() -
c.log_prob()).eval())
lower_bound = sgvb([a], [b, c], latent_axis=0)
self.assertEqual(lower_bound.get_shape().as_list(), [3])
np.testing.assert_almost_equal(lower_bound.eval(),
(a.log_prob() - b.log_prob() -
c.log_prob()).eval().reshape([3]))
lower_bound = sgvb([a], [b, c], latent_axis=[0, 1])
self.assertEqual(lower_bound.get_shape().as_list(), [])
np.testing.assert_almost_equal(
lower_bound.eval(),
np.mean((a.log_prob() - b.log_prob() - c.log_prob()).eval()))
def test_log_prob_with_group_events_ndims(self):
with self.get_session():
distrib = Normal(np.asarray(0., dtype=np.float32),
np.asarray([1.0, 2.0, 3.0], dtype=np.float32),
group_event_ndims=1)
observed = np.asarray([[-1., 1., 2.], [0., 0., 0.]])
t = StochasticTensor(distrib, observed=observed)
np.testing.assert_allclose(
t.log_prob(group_event_ndims=0).eval(),
distrib.log_prob(t, group_event_ndims=0).eval())
np.testing.assert_allclose(
t.log_prob(group_event_ndims=1).eval(),
distrib.log_prob(t, group_event_ndims=1).eval())
def test_set_attributes(self):
t = StochasticTensor(self.distrib, tf.constant([1., 2., 3.]))
self.assertTrue(hasattr(t, '_self_is_observed'))
self.assertFalse(hasattr(t.__wrapped__, '_self_is_observed'))
t._self_is_observed = 123
self.assertEqual(t._self_is_observed, 123)
self.assertFalse(hasattr(t.__wrapped__, '_self_is_observed'))
self.assertTrue(hasattr(t, 'log_prob'))
self.assertFalse(hasattr(t.__wrapped__, 'log_prob'))
t.log_prob = 456
self.assertEqual(t.log_prob, 456)
self.assertTrue(hasattr(t, 'log_prob'))
self.assertFalse(hasattr(t.__wrapped__, 'log_prob'))
self.assertTrue(hasattr(t, 'get_shape'))
self.assertTrue(hasattr(t.__wrapped__, 'get_shape'))
t.get_shape = 789
self.assertEqual(t.get_shape, 789)
self.assertEqual(t.__wrapped__.get_shape, 789)
self.assertTrue(hasattr(t, 'get_shape'))
self.assertTrue(hasattr(t.__wrapped__, 'get_shape'))
t.abc = 1001
self.assertEqual(t.abc, 1001)
self.assertEqual(t.__wrapped__.abc, 1001)
self.assertTrue(hasattr(t, 'abc'))
self.assertTrue(hasattr(t.__wrapped__, 'abc'))
t.__wrapped__.xyz = 2002
self.assertEqual(t.xyz, 2002)
self.assertEqual(t.__wrapped__.xyz, 2002)
self.assertTrue(hasattr(t, 'xyz'))
self.assertTrue(hasattr(t.__wrapped__, 'xyz'))
def test_prob_and_log_prob(self):
with self.get_session():
distrib = Normal(np.asarray(0., dtype=np.float32),
np.asarray([1.0, 2.0, 3.0], dtype=np.float32))
observed = np.arange(24, dtype=np.float32).reshape([4, 2, 3])
t = StochasticTensor(distrib, observed=observed)
np.testing.assert_almost_equal(t.log_prob().eval(),
distrib.log_prob(observed).eval())
np.testing.assert_almost_equal(t.prob().eval(),
distrib.prob(observed).eval())
def test_del_attributes(self):
t = StochasticTensor(self.distrib, tf.constant([1., 2., 3.]))
del t._self_is_observed
self.assertFalse(hasattr(t, '_self_is_observed'))
self.assertFalse(hasattr(t.__wrapped__, '_self_is_observed'))
t.abc = 1001
del t.abc
self.assertFalse(hasattr(t, 'abc'))
self.assertFalse(hasattr(t.__wrapped__, 'abc'))
t.__wrapped__.xyz = 2002
del t.xyz
self.assertFalse(hasattr(t, 'xyz'))
self.assertFalse(hasattr(t.__wrapped__, 'xyz'))
t.log_prob = 123
del t.log_prob
self.assertFalse(hasattr(t.__wrapped__, 'log_prob'))
self.assertNotEqual(t.log_prob, 123)
