def test_der(self): obj1 = make_autoTensor([[-3,3],[4,5]]) obj2 = make_autoTensor([[-3,3],[4,5]]) obj1.requires_grad = True obj = Add(obj1,obj2) obj.backprop(make_autoTensor([[1,1],[1,1]])) assert torch.sum(obj1.grad.value - make_autoTensor([[1,1],[1,1]]).value) == 0
def test_init(self): obj1 = make_autoTensor([[-3,3],[4,5]]) obj2 = make_autoTensor([[-3,3],[4,5]]) obj1.requires_grad = True obj = Add(obj1,obj2) assert obj.channels[0].autoVariable == obj1
def __radd__(self, other): return Add(self, make_autoTensor(other))
def Add(self, other): return Add(self, make_autoTensor(other))