22 Himmelblau.pdf

    1. import  numpy as np
    2. from    mpl_toolkits.mplot3d import Axes3D
    3. from    matplotlib import pyplot as plt
    4. import  torch
    8. def himmelblau(x):
    9.     return (x[0] ** 2 + x[1] - 11) ** 2 + (x[0] + x[1] ** 2 - 7) ** 2
    12. x = np.arange(-6, 6, 0.1)
    13. y = np.arange(-6, 6, 0.1)
    14. print('x,y range:', x.shape, y.shape)
    15. X, Y = np.meshgrid(x, y)
    16. print('X,Y maps:', X.shape, Y.shape)
    17. Z = himmelblau([X, Y])
    19. fig = plt.figure('himmelblau')
    20. ax = fig.gca(projection='3d')
    21. ax.plot_surface(X, Y, Z)
    22. ax.view_init(60, -30)
    23. ax.set_xlabel('x')
    24. ax.set_ylabel('y')
    25. plt.show()
    28. # [1., 0.], [-4, 0.], [4, 0.]
    29. x = torch.tensor([-4., 0.], requires_grad=True)
    30. optimizer = torch.optim.Adam([x], lr=1e-3)
    31. for step in range(20000):
    33.     pred = himmelblau(x)
    35.     optimizer.zero_grad()
    36.     pred.backward()
    37.     optimizer.step()
    39.     if step % 2000 == 0:
    40.         print ('step {}: x = {}, f(x) = {}'
    41.                .format(step, x.tolist(), pred.item()))