def test_elbo_with_transformed_distribution(self):
pyro.clear_param_store()
def model():
zero = Variable(torch.zeros(1))
one = Variable(torch.ones(1))
mu_latent = pyro.sample("mu_latent", dist.normal,
self.mu0, torch.pow(self.tau0, -0.5))
bijector = AffineExp(torch.pow(self.tau, -0.5), mu_latent)
x_dist = TransformedDistribution(dist.normal, bijector)
pyro.observe("obs0", x_dist, self.data[0], zero, one)
pyro.observe("obs1", x_dist, self.data[1], zero, one)
return mu_latent
def guide():
mu_q_log = pyro.param(
"mu_q_log",
Variable(
self.log_mu_n.data +
0.17,
requires_grad=True))
tau_q_log = pyro.param("tau_q_log", Variable(self.log_tau_n.data - 0.143,
requires_grad=True))
mu_q, tau_q = torch.exp(mu_q_log), torch.exp(tau_q_log)
pyro.sample("mu_latent", dist.normal, mu_q, torch.pow(tau_q, -0.5))
adam = optim.Adam({"lr": .0005, "betas": (0.96, 0.999)})
svi = SVI(model, guide, adam, loss="ELBO", trace_graph=False)
for k in range(12001):
svi.step()
mu_error = param_abs_error("mu_q_log", self.log_mu_n)
tau_error = param_abs_error("tau_q_log", self.log_tau_n)
self.assertEqual(0.0, mu_error, prec=0.05)
self.assertEqual(0.0, tau_error, prec=0.05)
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