def test_log_prob_value_and_group_ndims(self):
tf.set_random_seed(123456)
mean = tf.constant([0., 1., 2.], dtype=tf.float64)
normal = Normal(mean=mean, std=tf.constant(1., dtype=tf.float64))
y = tf.random_normal(shape=[2, 5, 3], dtype=tf.float64)
with self.test_session() as sess:
# test value_ndims = 0, group_ndims = 1
flow = QuadraticFlow(2., 5.)
flow.build(tf.zeros([2, 5, 3], dtype=tf.float64))
distrib = FlowDistribution(normal, flow)
self.assertEqual(distrib.value_ndims, 0)
x, log_det = flow.inverse_transform(y)
self.assertTupleEqual(get_static_shape(x), (2, 5, 3))
self.assertTupleEqual(get_static_shape(log_det), (2, 5, 3))
log_py = tf.reduce_sum(normal.log_prob(x) + log_det, axis=-1)
np.testing.assert_allclose(
*sess.run([distrib.log_prob(y, group_ndims=1), log_py]),
rtol=1e-5
)
# test value_ndims = 1, group_ndims = 0
flow = QuadraticFlow(2., 5., value_ndims=1)
flow.build(tf.zeros([2, 5, 3], dtype=tf.float64))
distrib = FlowDistribution(normal, flow)
self.assertEqual(distrib.value_ndims, 1)
x, log_det = flow.inverse_transform(y)
self.assertTupleEqual(get_static_shape(x), (2, 5, 3))
self.assertTupleEqual(get_static_shape(log_det), (2, 5))
log_py = normal.log_prob(x, group_ndims=1) + log_det
np.testing.assert_allclose(
*sess.run([distrib.log_prob(y, group_ndims=0), log_py]),
rtol=1e-5
)
# test value_ndims = 1, group_ndims = 2
flow = QuadraticFlow(2., 5., value_ndims=1)
flow.build(tf.zeros([2, 5, 3], dtype=tf.float64))
distrib = FlowDistribution(normal, flow)
self.assertEqual(distrib.value_ndims, 1)
x, log_det = flow.inverse_transform(y)
self.assertTupleEqual(get_static_shape(x), (2, 5, 3))
self.assertTupleEqual(get_static_shape(log_det), (2, 5))
log_py = tf.reduce_sum(
log_det + tf.reduce_sum(normal.log_prob(x), axis=-1))
np.testing.assert_allclose(
*sess.run([distrib.log_prob(y, group_ndims=2), log_py]),
rtol=1e-5
)
def test_sample_value_and_group_ndims(self):
tf.set_random_seed(123456)
mean = tf.constant([0., 1., 2.], dtype=tf.float64)
normal = Normal(mean=mean, std=tf.constant(1., dtype=tf.float64))
with self.test_session() as sess:
# test value_ndims = 0, group_ndims = 1
flow = QuadraticFlow(2., 5.)
distrib = FlowDistribution(normal, flow)
self.assertEqual(distrib.value_ndims, 0)
y = distrib.sample(n_samples=5, group_ndims=1)
self.assertTupleEqual(get_static_shape(y), (5, 3))
x, log_det = flow.inverse_transform(y)
self.assertTupleEqual(get_static_shape(x), (5, 3))
self.assertTupleEqual(get_static_shape(log_det), (5, 3))
log_py = tf.reduce_sum(normal.log_prob(x) + log_det, axis=-1)
np.testing.assert_allclose(*sess.run([y.log_prob(), log_py]),
rtol=1e-5)
# test value_ndims = 1, group_ndims = 0
flow = QuadraticFlow(2., 5., value_ndims=1)
distrib = FlowDistribution(normal, flow)
self.assertEqual(distrib.value_ndims, 1)
y = distrib.sample(n_samples=5, group_ndims=0)
self.assertTupleEqual(get_static_shape(y), (5, 3))
x, log_det = flow.inverse_transform(y)
self.assertTupleEqual(get_static_shape(x), (5, 3))
self.assertTupleEqual(get_static_shape(log_det), (5,))
log_py = log_det + tf.reduce_sum(normal.log_prob(x), axis=-1)
np.testing.assert_allclose(*sess.run([y.log_prob(), log_py]),
rtol=1e-5)
# test value_ndims = 1, group_ndims = 1
flow = QuadraticFlow(2., 5., value_ndims=1)
distrib = FlowDistribution(normal, flow)
self.assertEqual(distrib.value_ndims, 1)
y = distrib.sample(n_samples=5, group_ndims=1)
self.assertTupleEqual(get_static_shape(y), (5, 3))
x, log_det = flow.inverse_transform(y)
self.assertTupleEqual(get_static_shape(x), (5, 3))
self.assertTupleEqual(get_static_shape(log_det), (5,))
log_py = tf.reduce_sum(
log_det + tf.reduce_sum(normal.log_prob(x), axis=-1))
np.testing.assert_allclose(*sess.run([y.log_prob(), log_py]),
rtol=1e-5)
def test_sample(self):
tf.set_random_seed(123456)
mean = tf.constant([0., 1., 2.], dtype=tf.float64)
normal = Normal(mean=mean, std=tf.constant(1., dtype=tf.float64))
flow = QuadraticFlow(2., 5.)
distrib = FlowDistribution(normal, flow)
# test ordinary sample, is_reparameterized = None
y = distrib.sample(n_samples=5)
self.assertTrue(y.is_reparameterized)
grad = tf.gradients(y * 1., mean)[0]
self.assertIsNotNone(grad)
self.assertEqual(get_static_shape(y), (5, 3))
self.assertIsNotNone(y._self_log_prob)
x, log_det = flow.inverse_transform(y)
log_py = normal.log_prob(x) + log_det
with self.test_session() as sess:
np.testing.assert_allclose(*sess.run([log_py, y.log_prob()]),
rtol=1e-5)
# test stop gradient sample, is_reparameterized = False
y = distrib.sample(n_samples=5, is_reparameterized=False)
self.assertFalse(y.is_reparameterized)
grad = tf.gradients(y * 1., mean)[0]
self.assertIsNone(grad)
def test_log_prob(self):
mean = tf.constant([0., 1., 2.], dtype=tf.float64)
normal = Normal(mean=mean, std=tf.constant(1., dtype=tf.float64))
flow = QuadraticFlow(2., 5.)
flow.build(tf.constant(0., dtype=tf.float64))
distrib = FlowDistribution(normal, flow)
y = tf.constant([1., -1., 2.], dtype=tf.float64)
x, log_det = flow.inverse_transform(y)
log_py = normal.log_prob(x) + log_det
py = tf.exp(log_py)
log_prob = distrib.log_prob(y)
self.assertIsInstance(log_prob, FlowDistributionDerivedTensor)
self.assertIsInstance(log_prob.flow_origin, StochasticTensor)
self.assertIs(log_prob.flow_origin.distribution, normal)
prob = distrib.prob(y)
self.assertIsInstance(prob, FlowDistributionDerivedTensor)
self.assertIsInstance(prob.flow_origin, StochasticTensor)
self.assertIs(prob.flow_origin.distribution, normal)
with self.test_session() as sess:
np.testing.assert_allclose(
*sess.run([log_prob.flow_origin, x]), rtol=1e-5)
np.testing.assert_allclose(
*sess.run([log_py, log_prob]), rtol=1e-5)
np.testing.assert_allclose(
*sess.run([py, prob]), rtol=1e-5)
def test_log_prob(self):
mean = tf.constant([0., 1., 2.], dtype=tf.float64)
normal = Normal(mean=mean, std=tf.constant(1., dtype=tf.float64))
flow = QuadraticFlow(2., 5.)
flow.build(tf.constant(0., dtype=tf.float64))
distrib = FlowDistribution(normal, flow)
y = tf.constant([1., -1., 2.], dtype=tf.float64)
x, log_det = flow.inverse_transform(y)
log_py = normal.log_prob(x) + log_det
with self.test_session() as sess:
np.testing.assert_allclose(*sess.run([log_py,
distrib.log_prob(y)]),
rtol=1e-5)