adjusted conjugate model hyperparams
@@ -236,9 +236,9 @@ class GaussianPyramidTests(TestCase):
difficulty=0.4, model_permutation=False)
def test_elbo_nonreparameterized_model_permuted(self):
- for N in [2]:
+ for N in [2, 3]:
self.setup_pyramid(N)
- self.do_elbo_test(False, 30000, 0.001, 0.06, 0.95, difficulty=0.4, model_permutation=True)
+ self.do_elbo_test(False, 18000 * N, 0.0007, 0.06, 0.97, difficulty=0.4, model_permutation=True)
def calculate_variational_targets(self):
# calculate (some of the) variational parameters corresponding to exact posterior
@@ -411,7 +411,7 @@ class GaussianPyramidTests(TestCase):
node_flagged = True if self.which_nodes_reparam[i] == 1.0 else False
repa = True if reparameterized else node_flagged
latent_node = pyro.sample(node, latent_dist_node, reparameterized=repa,
- decaying_avg_baseline=True)
+ decaying_avg_baseline=True, baseline_beta=0.96)
latents_dict[node] = latent_node
return latents_dict['mu_latent_1']
