PyTorch——(6)2D函数优化实例

最小值点有4个

import numpy as np
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import pyplot as plt
import torch



def himmelblau(x):
    return (x[0] ** 2 + x[1] - 11) ** 2 + (x[0] + x[1] ** 2 - 7) ** 2


# x = np.arange(-6, 6, 0.1)
# y = np.arange(-6, 6, 0.1)
# print('x,y range:', x.shape, y.shape)
# X, Y = np.meshgrid(x, y)
# print('X,Y maps:', X.shape, Y.shape)
# Z = himmelblau([X, Y])
#
# fig = plt.figure('himmelblau')
# ax = fig.gca(projection='3d')
# ax.plot_surface(X, Y, Z)
# ax.view_init(60, -30)
# ax.set_xlabel('x')
# ax.set_ylabel('y')
# plt.show()


# [1., 0.], [-4, 0.], [4, 0.]
x = torch.tensor([-4., 0.], requires_grad=True)
# 设置优化器 (待优化变量，lr=学习率)
optimizer = torch.optim.Adam([x], lr=1e-3)
for step in range(20000):

    pred = himmelblau(x)
    # 把所有参数梯度缓存器置零
    optimizer.zero_grad()
    # 计算反向传播梯度
    pred.backward()
    optimizer.step()

    if step % 2000 == 0:
        print ('step {}: x = {}, f(x) = {}'
               .format(step, x.tolist(), pred.item()))