def guide():
loc_q = pyro.param("loc_q", self.analytic_loc_n.expand(2) + 0.094)
log_sig_q = pyro.param("log_sig_q",
self.analytic_log_sig_n.expand(2) - 0.07)
sig_q = torch.exp(log_sig_q)
trivial_baseline = pyro.module("loc_baseline", pt_loc_baseline)
baseline_value = trivial_baseline(torch.ones(1)).squeeze()
loc_latent = pyro.sample(
"loc_latent",
fakes.NonreparameterizedNormal(loc_q, sig_q).to_event(1),
infer=dict(baseline=dict(baseline_value=baseline_value)),
)
for i in pyro.plate("outer", 3):
with pyro.plate("inner_%d" % i, 4 - i):
for k in range(n_superfluous_top + n_superfluous_bottom):
z_baseline = pyro.module(
"z_baseline_%d_%d" % (i, k),
pt_superfluous_baselines[3 * k + i],
)
baseline_value = z_baseline(loc_latent.detach())
mean_i = pyro.param("mean_%d_%d" % (i, k),
0.5 * torch.ones(4 - i))
z_i_k = pyro.sample(
"z_%d_%d" % (i, k),
fakes.NonreparameterizedNormal(mean_i, 1),
infer=dict(baseline=dict(
baseline_value=baseline_value)),
)
assert z_i_k.shape == (4 - i, )
def model():
loc_latent = pyro.sample(
"loc_latent",
fakes.NonreparameterizedNormal(self.loc0,
torch.pow(self.lam0,
-0.5)).to_event(1),
)
for i in pyro.plate("outer", 3):
x_i = self.data_as_list[i]
with pyro.plate("inner_%d" % i, x_i.size(0)):
for k in range(n_superfluous_top):
z_i_k = pyro.sample(
"z_%d_%d" % (i, k),
fakes.NonreparameterizedNormal(0, 1).expand_by(
[4 - i]),
)
assert z_i_k.shape == (4 - i, )
obs_i = pyro.sample(
"obs_%d" % i,
dist.Normal(loc_latent, torch.pow(self.lam,
-0.5)).to_event(1),
obs=x_i,
)
assert obs_i.shape == (4 - i, 2)
for k in range(n_superfluous_top,
n_superfluous_top + n_superfluous_bottom):
z_i_k = pyro.sample(
"z_%d_%d" % (i, k),
fakes.NonreparameterizedNormal(0, 1).expand_by(
[4 - i]),
)
assert z_i_k.shape == (4 - i, )
def model():
loc_latent = pyro.sample("loc_latent",
fakes.NonreparameterizedNormal(self.loc0, torch.pow(self.lam0, -0.5))
.to_event(1))
for i in pyro.plate("outer", self.n_outer):
for j in pyro.plate("inner_%d" % i, self.n_inner):
pyro.sample("obs_%d_%d" % (i, j),
dist.Normal(loc_latent, torch.pow(self.lam, -0.5)).to_event(1),
obs=self.data[i][j])
def guide():
loc_q = pyro.param("loc_q", self.analytic_loc_n.expand(2) + 0.234)
log_sig_q = pyro.param("log_sig_q",
self.analytic_log_sig_n.expand(2) - 0.27)
sig_q = torch.exp(log_sig_q)
pyro.sample("loc_latent", fakes.NonreparameterizedNormal(loc_q, sig_q).to_event(1),
infer=dict(baseline=dict(use_decaying_avg_baseline=True)))
for i in pyro.plate("outer", self.n_outer):
for j in pyro.plate("inner_%d" % i, self.n_inner):
pass
def guide():
loc_q = pyro.param(
"loc_q",
torch.tensor(self.analytic_loc_n.expand(2) + 0.234,
requires_grad=True))
log_sig_q = pyro.param(
"log_sig_q",
torch.tensor(self.analytic_log_sig_n.expand(2) - 0.27,
requires_grad=True))
sig_q = torch.exp(log_sig_q)
pyro.sample(
"loc_latent",
fakes.NonreparameterizedNormal(loc_q, sig_q).independent(1),
infer=dict(baseline=dict(use_decaying_avg_baseline=True)))
for i in pyro.irange("outer", self.n_outer):
for j in pyro.irange("inner_%d" % i, self.n_inner):
pass
