def test_compose_transform_with_intermediates(ts):
transform = transforms.ComposeTransform(ts)
x = random.normal(random.PRNGKey(2), (7, 5))
y, intermediates = transform.call_with_intermediates(x)
logdet = transform.log_abs_det_jacobian(x, y, intermediates)
assert_allclose(y, transform(x))
assert_allclose(logdet, transform.log_abs_det_jacobian(x, y))
def test_iaf():
# test for substitute logic for exposed methods `sample_posterior` and `get_transforms`
N, dim = 3000, 3
data = random.normal(random.PRNGKey(0), (N, dim))
true_coefs = jnp.arange(1.0, dim + 1.0)
logits = jnp.sum(true_coefs * data, axis=-1)
labels = dist.Bernoulli(logits=logits).sample(random.PRNGKey(1))
def model(data, labels):
coefs = numpyro.sample("coefs",
dist.Normal(jnp.zeros(dim), jnp.ones(dim)))
offset = numpyro.sample("offset", dist.Uniform(-1, 1))
logits = offset + jnp.sum(coefs * data, axis=-1)
return numpyro.sample("obs", dist.Bernoulli(logits=logits), obs=labels)
adam = optim.Adam(0.01)
rng_key_init = random.PRNGKey(1)
guide = AutoIAFNormal(model)
svi = SVI(model, guide, adam, Trace_ELBO())
svi_state = svi.init(rng_key_init, data, labels)
params = svi.get_params(svi_state)
x = random.normal(random.PRNGKey(0), (dim + 1, ))
rng_key = random.PRNGKey(1)
actual_sample = guide.sample_posterior(rng_key, params)
actual_output = guide._unpack_latent(guide.get_transform(params)(x))
flows = []
for i in range(guide.num_flows):
if i > 0:
flows.append(transforms.PermuteTransform(
jnp.arange(dim + 1)[::-1]))
arn_init, arn_apply = AutoregressiveNN(
dim + 1,
[dim + 1, dim + 1],
permutation=jnp.arange(dim + 1),
skip_connections=guide._skip_connections,
nonlinearity=guide._nonlinearity,
)
arn = partial(arn_apply, params["auto_arn__{}$params".format(i)])
flows.append(InverseAutoregressiveTransform(arn))
flows.append(guide._unpack_latent)
transform = transforms.ComposeTransform(flows)
_, rng_key_sample = random.split(rng_key)
expected_sample = transform(
dist.Normal(jnp.zeros(dim + 1), 1).sample(rng_key_sample))
expected_output = transform(x)
assert_allclose(actual_sample["coefs"], expected_sample["coefs"])
assert_allclose(
actual_sample["offset"],
transforms.biject_to(constraints.interval(-1, 1))(
expected_sample["offset"]),
)
check_eq(actual_output, expected_output)
rng_key = random.PRNGKey(0)
assert_allclose(loc + scale * dist1.sample(rng_key), dist2.sample(rng_key))
intermediates = dist2.sample_with_intermediates(rng_key)
assert len(intermediates) == 2
def _make_iaf(input_dim, hidden_dims, rng_key):
arn_init, arn = AutoregressiveNN(input_dim, hidden_dims, param_dims=[1, 1])
_, init_params = arn_init(rng_key, (input_dim,))
return InverseAutoregressiveTransform(partial(arn, init_params))
@pytest.mark.parametrize('ts', [
[transforms.PowerTransform(0.7), transforms.AffineTransform(2., 3.)],
[transforms.ExpTransform()],
[transforms.ComposeTransform([transforms.AffineTransform(-2, 3),
transforms.ExpTransform()]),
transforms.PowerTransform(3.)],
[_make_iaf(5, hidden_dims=[10], rng_key=random.PRNGKey(0)),
transforms.PermuteTransform(np.arange(5)[::-1]),
_make_iaf(5, hidden_dims=[10], rng_key=random.PRNGKey(1))]
])
def test_compose_transform_with_intermediates(ts):
transform = transforms.ComposeTransform(ts)
x = random.normal(random.PRNGKey(2), (7, 5))
y, intermediates = transform.call_with_intermediates(x)
logdet = transform.log_abs_det_jacobian(x, y, intermediates)
assert_allclose(y, transform(x))
assert_allclose(logdet, transform.log_abs_det_jacobian(x, y))
def test_unpack_transform():
