def init_loc_fn(site):
value = init_to_median(site, num_samples=50)
# we also make sure the initial sigma is not too large.
# (otherwise we run the danger of getting stuck in bad local optima during optimization).
if site["name"] == "sigma":
value = 0.1 * value
return value
value = torch.randn(()).exp() * 10
kernel = NUTS(model,
init_strategy=partial(init_to_value, values={"x": value}))
kernel.setup(warmup_steps=10)
assert_close(value, kernel.initial_params['x'].exp())
@pytest.mark.parametrize("init_strategy", [
init_to_feasible,
init_to_mean,
init_to_median,
init_to_sample,
init_to_uniform,
init_to_value,
init_to_feasible(),
init_to_mean(),
init_to_median(num_samples=4),
init_to_sample(),
init_to_uniform(radius=0.1),
init_to_value(values={"x": torch.tensor(3.)}),
init_to_generated(
generate=lambda: init_to_value(values={"x": torch.rand(())})),
],
ids=str_erase_pointers)
def test_init_strategy_smoke(init_strategy):
def model():
pyro.sample("x", dist.LogNormal(0, 1))
kernel = NUTS(model, init_strategy=init_strategy)
kernel.setup(warmup_steps=10)
