def test_backward(self):
x0 = Variable(np.array(1.0))
x1 = Variable(np.array(1.0))
t = add(x0, x1)
y = add(x0, t)
y.backward()
self.assertEqual(x0.grad.data, np.array(2.0))
self.assertEqual(x1.grad.data, np.array(1.0))
def test_retain_grad_only_the_firts_inputs(self):
x0 = Variable(np.array(2.0))
x1 = Variable(np.array(3.0))
t = add(x0, x1)
y = add(x0, t)
y.backward()
self.assertIsNone(y.grad)
self.assertIsNone(t.grad)
self.assertEqual(x0.grad.data, 2.0)
self.assertEqual(x1.grad.data, 1.0)
def test_backward_sets_grad_on_inputs(self):
x0 = Variable(np.array(2.0))
x1 = Variable(np.array(3.0))
y = add(x0, x1)
y.backward()
self.assertEqual(x0.grad.data, np.array(1.0))
self.assertEqual(x1.grad.data, np.array(1.0))
def test_generation(self):
x = Variable(np.array(2.0))
a = F.square(x)
y = add(F.square(a), F.square(a))
y.backward()
self.assertEqual(y.data, np.array(32.0))
self.assertEqual(x.grad.data, np.array(64.0))
def test_forward(self):
x = Variable(np.array(2.0))
y = Variable(np.array(3.0))
z = add(F.square(x), F.square(y))
z.backward()
self.assertEqual(z.data, np.array(13.0))
self.assertTrue(isinstance(x.grad, Variable))
a = Variable(np.array(2.0))
b = add(a, a)
b.backward()
a.cleargrad()
b = add(add(a, a), a)
b.backward()
def test_add_backward(self, culc_object):
x0 = culc_object[0]
x1 = culc_object[1]
y = add(x0, x1)
y.backward()
assert x0.grad.data == 1 and x1.grad.data == 1
def test_add(self, culc_object):
assert add(culc_object[0], culc_object[1]).data == 5
def test_two_branch_diff(self):
x = Variable(np.array(2.0))
a = square(x)
y = add(square(a), square(a))
y.backward()
self.assertEqual(x.grad.data, np.array(64.0))
def test_forward(self):
x0 = Variable(np.array(2.0))
x1 = Variable(np.array(3.0))
y = add(x0, x1)
self.assertEqual(y.data, np.array(5.0))