def test_complex_graph_backward():
x = Variable(np.array(2.0))
a = square(x)
y = add(square(a), square(a))
y.backward()
assert y.data == 32.0
assert x.grad == 64.0
def test_square_backward():
x = Variable(np.array(2.0))
y = Variable(np.array(3.0))
z = add(square(x), square(y))
z.backward()
print(z.data)
print(x.grad)
print(y.grad)
def test_gradient_check(self):
x = Variable(np.random.rand(1))
y = square(x)
y.backward()
num_grad = numerical_diff(square, x)
flg = np.allclose(x.grad, num_grad)
self.assertTrue(flg)
def test_backward(self):
x = Variable(np.array(3.0))
y = square(x)
y.backward()
expected = np.array(6.0)
self.assertEqual(x.grad, expected)
def test_forward(self):
x = Variable(np.array(2.0))
y = square(x)
expected = np.array(4.0)
self.assertEqual(y.data, expected)
def test_step_18():
with using_config('enable_backprop', False):
x = Variable(np.array(2.0))
y = square(x)
assert y.grad is None