def test_backward_with_broadcast(self):
x0 = Variable(np.array([10, 11, 12]))
x1 = Variable(np.array([5]))
y = sub(x0, x1)
y.backward()
assert_equal(x0.grad.data, np.array([1, 1, 1]))
assert_equal(x1.grad.data, np.array([-3]))
def test_backward(self):
a = Variable(np.array(3.0))
b = Variable(np.array(2.0))
y = sub(a, b)
y.backward()
self.assertEqual(a.grad.data, np.array(1.0))
self.assertEqual(b.grad.data, np.array(-1.0))
def test_sub_backward(self, culc_object):
x0 = culc_object[0]
x1 = culc_object[1]
y = sub(x0, x1)
y.backward()
assert x0.grad.data == 1 and x1.grad.data == -1
def test_sub(self, culc_object):
assert sub(culc_object[0], culc_object[1]).data == -1
def test_forward_with_broadcast(self):
x0 = Variable(np.arange(100).reshape((100, 1)))
x1 = Variable(np.ones((100, 1)))
y = sub(x0, x1)
assert_equal(y.data, np.arange(-1, 99).reshape(100, 1))
def test_forward(self):
a = Variable(np.array(3.0))
b = Variable(np.array(2.0))
y = sub(a, b)
self.assertEqual(y.data, np.array(1.0))