def test_optim_rosenbrock(self):
x0 = Variable(np.array(0.0))
x1 = Variable(np.array(2.0))
lr = 0.001
iters = 50000
for i in range(iters):
y = rosenbrock(x0, x1)
x0.cleargrad()
x1.cleargrad()
y.backward()
x0.data = x0.data - lr * x0.grad.data
x1.data = x1.data - lr * x1.grad.data
# 50000回回すと、誤差は10の-8乗以下になる
self.assertEqual((round(x0.data, 8), round(x1.data, 8)), (1.0, 1.0))
def test_newton_method(self):
def f(x):
y = x**4 - 2 * x**2
return y
x = Variable(np.array(2.0))
iters = 10
for i in range(iters):
y = f(x)
x.cleargrad()
y.backward(create_graph=True)
gx = x.grad
x.cleargrad()
gx.backward()
gx2 = x.grad
x.data = x.data - gx.data / gx2.data
self.assertEqual(x.data, 1.0)