def test_backward_with_broadcast(self):
x0 = Variable(np.array([10, 11, 12]))
x1 = Variable(np.array([5]))
y = mul(x0, x1)
y.backward()
assert_equal(x0.grad.data, np.array([5, 5, 5]))
assert_equal(x1.grad.data, np.array([10+11+12]))
def test_backward(self):
a = Variable(np.array(3.0))
b = Variable(np.array(2.0))
y = mul(a, b)
y.backward()
self.assertEqual(a.grad.data, np.array(2.0))
self.assertEqual(b.grad.data, np.array(3.0))
def test_mul_backward_overload(self, culc_object):
x0 = culc_object[0]
x1 = culc_object[1]
y = mul(x0, x1)
y.backward()
assert x0.grad.data == 3 and x1.grad.data == 2
def test_mul(self, culc_object):
x0 = culc_object[0]
x1 = culc_object[1]
assert mul(x0, x1).data == 6
def test_forward(self):
a = Variable(np.array(3.0))
b = Variable(np.array(2.0))
y = mul(a, b)
self.assertEqual(y.data, np.array(6.0))