def make_simple_gluon_model(self):
net = self.make_net()
m = Model()
m.x = Variable(shape=(1, 1))
m.f = MXFusionGluonFunction(net, num_outputs=1)
m.y = m.f(m.x)
return m
def test_set_parameters(self):
class SetValue(InferenceAlgorithm):
def __init__(self, x, y, model, observed, extra_graphs=None):
self.x_val = x
self.y_val = y
super(SetValue, self).__init__(
model=model, observed=observed, extra_graphs=extra_graphs)
def compute(self, F, variables):
self.set_parameter(variables, self.model.x, self.x_val)
self.set_parameter(variables, self.model.y, self.y_val)
m = Model()
m.x = Variable(shape=(2,))
m.y = Variable(shape=(3, 4))
dtype = 'float64'
np.random.seed(0)
x_np = np.random.rand(2)
y_np = np.random.rand(3, 4)
x_mx = mx.nd.array(x_np, dtype=dtype)
y_mx = mx.nd.array(y_np, dtype=dtype)
infr = Inference(SetValue(x_mx, y_mx, m, []), dtype=dtype)
infr.run()
x_res = infr.params[m.x]
y_res = infr.params[m.y]
assert np.allclose(x_res.asnumpy(), x_np)
assert np.allclose(y_res.asnumpy(), y_np)
def test_gluon_parameters(self):
self.setUp()
m = Model()
m.x = Variable(shape=(1, 1))
m.f = MXFusionGluonFunction(self.net, num_outputs=1)
m.y = m.f(m.x)
infr = Inference(ForwardSamplingAlgorithm(m, observed=[m.x]))
infr.run(x=mx.nd.ones((1, 1)))
assert all([
v.uuid in infr.params.param_dict for v in m.f.parameters.values()
])