def _make_gluon_function_evaluation_rand_param(self, dtype, broadcastable):
class Dot(HybridBlock):
def __init__(self, params=None, prefix=None):
super(Dot, self).__init__(params=params, prefix=prefix)
with self.name_scope():
self.const = self.params.get('const',
shape=(1, ),
dtype=dtype,
init=Zero())
def hybrid_forward(self, F, a, b, const):
return F.broadcast_add(F.linalg.gemm2(a, b), const)
dot = Dot(prefix='dot_')
dot.initialize()
dot.hybridize()
print(dot.collect_params())
func_wrapper = MXFusionGluonFunction(dot,
1,
dtype=dtype,
broadcastable=broadcastable)
from mxfusion.components.distributions.normal import Normal
rand_var = Normal.define_variable(shape=(1, ))
out = func_wrapper(Variable(shape=(3, 4)),
Variable(shape=(4, 5)),
dot_const=rand_var)
return out.factor
def _make_gluon_function_evaluation(self, dtype, broadcastable):
class Dot(HybridBlock):
def hybrid_forward(self, F, a, b):
return F.linalg.gemm2(a, b)
dot = Dot(prefix='dot')
dot.initialize()
dot.hybridize()
func_wrapper = MXFusionGluonFunction(dot, 1, dtype=dtype,
broadcastable=broadcastable)
out = func_wrapper(Variable(shape=(3, 4)), Variable(shape=(4, 5)))
return out.factor
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()
])
def test_success(self):
self.setUp()
f = MXFusionGluonFunction(self.net, num_outputs=1)
x = Variable()
y = f(x)