def main(args):
# Define a basic model with a single Normal latent random variable `loc`
# and a batch of Normally distributed observations.
def model(data):
loc = pyro.sample("loc", dist.Normal(0., 1.))
with pyro.plate("data", len(data), dim=-1):
pyro.sample("obs", dist.Normal(loc, 1.), obs=data)
# Define a guide (i.e. variational distribution) with a Normal
# distribution over the latent random variable `loc`.
def guide(data):
guide_loc = pyro.param("guide_loc", torch.tensor(0.))
guide_scale = pyro.param("guide_scale",
torch.tensor(1.),
constraint=constraints.positive)
pyro.sample("loc", dist.Normal(guide_loc, guide_scale))
# Generate some data.
torch.manual_seed(0)
data = torch.randn(100) + 3.0
# Because the API in minipyro matches that of Pyro proper,
# training code works with generic Pyro implementations.
with pyro_backend(args.backend), interpretation(monte_carlo):
# Construct an SVI object so we can do variational inference on our
# model/guide pair.
Elbo = infer.JitTrace_ELBO if args.jit else infer.Trace_ELBO
elbo = Elbo()
adam = optim.Adam({"lr": args.learning_rate})
svi = infer.SVI(model, guide, adam, elbo)
# Basic training loop
pyro.get_param_store().clear()
for step in range(args.num_steps):
loss = svi.step(data)
if args.verbose and step % 100 == 0:
print("step {} loss = {}".format(step, loss))
# Report the final values of the variational parameters
# in the guide after training.
if args.verbose:
for name in pyro.get_param_store():
value = pyro.param(name).data
print("{} = {}".format(name, value.detach().cpu().numpy()))
# For this simple (conjugate) model we know the exact posterior. In
# particular we know that the variational distribution should be
# centered near 3.0. So let's check this explicitly.
assert (pyro.param("guide_loc") - 3.0).abs() < 0.1
def build_svi(model, guide, elbo):
pyro.get_param_store().clear()
adam = optim.Adam({"lr": 1e-6})
return infer.SVI(model, guide, adam, elbo)