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))