def model():
locs = pyro.param("locs",
ops.randn(3),
constraint=dist.constraints.real)
scales = pyro.param("scales",
ops.exp(ops.randn(3)),
constraint=dist.constraints.positive)
p = ops.tensor([0.5, 0.3, 0.2])
x = pyro.sample("x", dist.Categorical(p))
pyro.sample("obs", dist.Normal(locs[x], scales[x]), obs=data)
def test_local_param_ok(backend, jit):
data = ops.randn(10)
def model():
locs = pyro.param("locs", ops.tensor([-1., 0., 1.]))
with pyro.plate("plate", len(data), dim=-1):
x = pyro.sample("x", dist.Categorical(ops.ones(3) / 3))
pyro.sample("obs", dist.Normal(locs[x], 1.), obs=data)
def guide():
with pyro.plate("plate", len(data), dim=-1):
p = pyro.param("p", ops.ones(len(data), 3) / 3, event_dim=1)
pyro.sample("x", dist.Categorical(p))
return p
Elbo = infer.JitTrace_ELBO if jit else infer.Trace_ELBO
elbo = Elbo(ignore_jit_warnings=True)
assert_ok(model, guide, elbo)
# Check that pyro.param() can be called without init_value.
expected = guide()
actual = pyro.param("p")
assert ops.allclose(actual, expected)
def model(data):
loc = pyro.param("loc", ops.tensor(0.0))
pyro.sample("x", dist.Normal(loc, 1.), obs=data)
def model(data):
p = pyro.param("p", ops.tensor(0.5))
pyro.sample("x", dist.Bernoulli(p), obs=data)
def model(data=None):
loc = pyro.param("loc", ops.tensor(2.0))
scale = pyro.param("scale", ops.tensor(1.0))
with pyro.plate("data", 1000, dim=-1):
x = pyro.sample("x", dist.Normal(loc, scale), obs=data)
return x
def model(data=None):
loc = pyro.param("loc", ops.tensor(2.0))
scale = pyro.param("scale", ops.tensor(1.0))
x = pyro.sample("x", dist.Normal(loc, scale), obs=data)
return x
def guide():
loc = pyro.param("loc", ops.tensor(0.))
x = pyro.sample("x", dist.Normal(loc, 1.))
pyro.sample("y", dist.Normal(x, 1.))
def guide():
q = pyro.param("q",
ops.exp(ops.randn(3)),
constraint=dist.constraints.simplex)
pyro.sample("x", dist.Categorical(q))
def guide():
with pyro.plate("plate", len(data), dim=-1):
p = pyro.param("p", ops.ones(len(data), 3) / 3, event_dim=1)
pyro.sample("x", dist.Categorical(p))
return p
def model():
locs = pyro.param("locs", ops.tensor([-1., 0., 1.]))
with pyro.plate("plate", len(data), dim=-1):
x = pyro.sample("x", dist.Categorical(ops.ones(3) / 3))
pyro.sample("obs", dist.Normal(locs[x], 1.), obs=data)
def guide():
loc = pyro.param("loc", ops.tensor(0.))
scale = pyro.param("scale", ops.tensor(1.))
with pyro.plate("plate_outer", data.shape[-1], dim=-1):
pyro.sample("x", dist.Normal(loc, scale))
def guide():
p = pyro.param("p", ops.tensor([0.5, 0.3, 0.2]))
with pyro.plate("plate", len(data), dim=-1):
pyro.sample("x", dist.Categorical(p))
def model():
locs = pyro.param("locs", ops.tensor([0.2, 0.3, 0.5]))
p = ops.tensor([0.2, 0.3, 0.5])
with pyro.plate("plate", len(data), dim=-1):
x = pyro.sample("x", dist.Categorical(p))
pyro.sample("obs", dist.Normal(locs[x], 1.), obs=data)
