def test_sanity_check():
x = Value(-4.0, _op="x")
z = 2 * x + 2 + x
q = z.relu() + z * x
h = (z * z).relu()
y = h + q + q * x
y.backward()
xmg, ymg = x, y
x = torch.Tensor([-4.0]).double()
x.requires_grad = True
z = 2 * x + 2 + x
q = z.relu() + z * x
h = (z * z).relu()
y = h + q + q * x
y.backward()
xpt, ypt = x, y
# forward pass went well
assert ymg.data == ypt.data.item()
print(f"{ymg.data = }\n{ypt.data.item() = }\n")
# backward pass went well
print(f"{xmg.grad = }\n{xpt.grad.item() = }\n")
assert xmg.grad == xpt.grad.item()
def test_more_ops():
a = Value(-4.0)
b = Value(2.0)
c = a + b
d = a * b + b**3
c += c + 1
c += 1 + c + (-a)
d += d * 2 + (b + a).relu()
d += 3 * d + (b - a).relu()
e = c - d
f = e**2
g = f / 2.0
g += 10.0 / f
g.backward()
amg, bmg, gmg = a, b, g
a = torch.Tensor([-4.0]).double()
b = torch.Tensor([2.0]).double()
a.requires_grad = True
b.requires_grad = True
c = a + b
d = a * b + b**3
c = c + c + 1
c = c + 1 + c + (-a)
d = d + d * 2 + (b + a).relu()
d = d + 3 * d + (b - a).relu()
e = c - d
f = e**2
g = f / 2.0
g = g + 10.0 / f
g.backward()
apt, bpt, gpt = a, b, g
tol = 1e-6
# forward pass went
print(f"{gmg.data = }\n{gpt.data.item() = }\n")
print(f"{amg.grad = }\n{apt.grad.item() = }\n")
print(f"{bmg.grad = }\n{bpt.grad.item() = }\n")
assert abs(gmg.data - gpt.data.item()) < tol
# backward pass went well
assert abs(amg.grad - apt.grad.item()) < tol
assert abs(bmg.grad - bpt.grad.item()) < tol
def test_sanity_check():
x = Value(-4.0)
z = 2 * x + 2 + x
q = z.relu() + z * x
h = (z * z).relu()
y = h + q + q * x
y.backward()
xmg, ymg = x, y
x = torch.Tensor([-4.0])
x.requires_grad = True
z = 2 * x + 2 + x
q = z.relu() + z * x
h = (z * z).relu()
y = h + q + q * x
y.backward()
xpt, ypt = x, y
# forward pass went well
assert ymg.data == ypt.data.item()
# backward pass went well
assert xmg.grad == xpt.grad.item()