def test_compose_transform_with_intermediates(transforms):
transform = constraints.ComposeTransform(transforms)
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 = np.arange(1., dim + 1.)
logits = np.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(np.zeros(dim),
np.ones(dim)))
offset = numpyro.sample('offset', dist.Uniform(-1, 1))
logits = offset + np.sum(coefs * data, axis=-1)
return numpyro.sample('obs', dist.Bernoulli(logits=logits), obs=labels)
adam = optim.Adam(0.01)
rng_init = random.PRNGKey(1)
guide = AutoIAFNormal(model)
svi = SVI(model, guide, elbo, adam)
svi_state = svi.init(rng_init,
model_args=(data, labels),
guide_args=(data, labels))
params = svi.get_params(svi_state)
x = random.normal(random.PRNGKey(0), (dim + 1, ))
rng = random.PRNGKey(1)
actual_sample = guide.sample_posterior(rng, params)
actual_output = guide.get_transform(params)(x)
flows = []
for i in range(guide.num_flows):
if i > 0:
flows.append(constraints.PermuteTransform(
np.arange(dim + 1)[::-1]))
arn_init, arn_apply = AutoregressiveNN(
dim + 1, [dim + 1, dim + 1],
permutation=np.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))
transform = constraints.ComposeTransform(flows)
rng_seed, rng_sample = random.split(rng)
expected_sample = guide.unpack_latent(
transform(dist.Normal(np.zeros(dim + 1), 1).sample(rng_sample)))
expected_output = transform(x)
assert_allclose(actual_sample['coefs'], expected_sample['coefs'])
assert_allclose(
actual_sample['offset'],
constraints.biject_to(constraints.interval(-1, 1))(
expected_sample['offset']))
assert_allclose(actual_output, expected_output)
assert len(intermediates) == 2
def _make_iaf(input_dim, hidden_dims, rng):
arn_init, arn = AutoregressiveNN(input_dim, hidden_dims, param_dims=[1, 1])
_, init_params = arn_init(rng, (input_dim, ))
return InverseAutoregressiveTransform(partial(arn, init_params))
@pytest.mark.parametrize(
'transforms',
[[constraints.PowerTransform(0.7),
constraints.AffineTransform(2., 3.)], [constraints.ExpTransform()],
[
constraints.ComposeTransform(
[constraints.AffineTransform(-2, 3),
constraints.ExpTransform()]),
constraints.PowerTransform(3.)
],
[
_make_iaf(5, hidden_dims=[10], rng=random.PRNGKey(0)),
constraints.PermuteTransform(np.arange(5)[::-1]),
_make_iaf(5, hidden_dims=[10], rng=random.PRNGKey(1))
]])
def test_compose_transform_with_intermediates(transforms):
transform = constraints.ComposeTransform(transforms)
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))