def test_invert_flow(self):
with self.test_session() as sess:
# test invert a normal flow
flow = QuadraticFlow(2., 5.)
inv_flow = flow.invert()
self.assertIsInstance(inv_flow, InvertFlow)
self.assertEqual(inv_flow.x_value_ndims, 0)
self.assertEqual(inv_flow.y_value_ndims, 0)
self.assertFalse(inv_flow.require_batch_dims)
test_x = np.arange(12, dtype=np.float32) + 1.
test_y, test_log_det = quadratic_transform(npyops, test_x, 2., 5.)
self.assertFalse(flow._has_built)
y, log_det_y = inv_flow.inverse_transform(tf.constant(test_x))
self.assertTrue(flow._has_built)
np.testing.assert_allclose(sess.run(y), test_y)
np.testing.assert_allclose(sess.run(log_det_y), test_log_det)
invertible_flow_standard_check(self, inv_flow, sess, test_y)
# test invert an InvertFlow
inv_inv_flow = inv_flow.invert()
self.assertIs(inv_inv_flow, flow)
# test use with FlowDistribution
normal = Normal(mean=1., std=2.)
inv_flow = QuadraticFlow(2., 5.).invert()
distrib = FlowDistribution(normal, inv_flow)
distrib_log_det = distrib.log_prob(test_x)
np.testing.assert_allclose(*sess.run(
[distrib_log_det,
normal.log_prob(test_y) + test_log_det]))
def test_with_normal(self):
mean = np.random.normal(size=[4, 5]).astype(np.float64)
logstd = np.random.normal(size=mean.shape).astype(np.float64)
x = np.random.normal(size=[3, 4, 5])
with self.test_session() as sess:
normal = Normal(mean=mean, logstd=logstd)
distrib = normal.batch_ndims_to_value(1)
self.assertIsInstance(distrib, BatchToValueDistribution)
self.assertEqual(distrib.value_ndims, 1)
self.assertEqual(distrib.get_batch_shape().as_list(), [4])
self.assertEqual(list(sess.run(distrib.batch_shape)), [4])
self.assertEqual(distrib.dtype, tf.float64)
self.assertTrue(distrib.is_continuous)
self.assertTrue(distrib.is_reparameterized)
self.assertIs(distrib.base_distribution, normal)
log_prob = distrib.log_prob(x)
log_prob2 = distrib.log_prob(x, group_ndims=1)
self.assertEqual(get_static_shape(log_prob), (3, 4))
self.assertEqual(get_static_shape(log_prob2), (3, ))
np.testing.assert_allclose(*sess.run(
[log_prob, normal.log_prob(x, group_ndims=1)]))
np.testing.assert_allclose(*sess.run(
[log_prob2, normal.log_prob(x, group_ndims=2)]))
prob = distrib.prob(x)
prob2 = distrib.prob(x, group_ndims=1)
self.assertEqual(get_static_shape(prob), (3, 4))
self.assertEqual(get_static_shape(prob2), (3, ))
np.testing.assert_allclose(
*sess.run([prob, normal.prob(x, group_ndims=1)]))
np.testing.assert_allclose(
*sess.run([prob2, normal.prob(x, group_ndims=2)]))
sample = distrib.sample(3, compute_density=False)
sample2 = distrib.sample(3, compute_density=True, group_ndims=1)
log_prob = sample.log_prob()
log_prob2 = sample2.log_prob()
self.assertEqual(get_static_shape(log_prob), (3, 4))
self.assertEqual(get_static_shape(log_prob2), (3, ))
np.testing.assert_allclose(*sess.run(
[log_prob, normal.log_prob(sample, group_ndims=1)]))
np.testing.assert_allclose(*sess.run(
[log_prob2, normal.log_prob(sample2, group_ndims=2)]))