Pytorch optimizer
作者：互联网
Minima
\[f(x,y)=(x^2+y-11)^2+(x+y^2-7)^2 \]
%matplotlib inline
import numpy as np, torch, torch.nn.functional as F
from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
# plot
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 range: (120,) (120,)
X, Y = np.meshgrid(x, y)
print("X, Y maps:", X.shape, Y.shape)
Z = himmelblau([X, Y])
X, Y maps: (120, 120) (120, 120)
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()
# Gradient Descent
#[1., 0] [-4, 0.] [4, 0.]
x = torch.tensor([0., 0.], requires_grad=True)
opt = torch.optim.Adam([x], lr=1e-3)
for step in range(20001):
pred = himmelblau(x)
opt.zero_grad()
pred.backward()
opt.step()
if step % 2000 == 0:
print("step {}: x = {}, f(x) = {}".format(step, x.tolist(), pred.item()))
step 0: x = [0.0009999999310821295, 0.0009999999310821295], f(x) = 170.0
step 2000: x = [2.3331806659698486, 1.9540694952011108], f(x) = 13.730916023254395
step 4000: x = [2.9820079803466797, 2.0270984172821045], f(x) = 0.014858869835734367
step 6000: x = [2.999983549118042, 2.0000221729278564], f(x) = 1.1074007488787174e-08
step 8000: x = [2.9999938011169434, 2.0000083446502686], f(x) = 1.5572823031106964e-09
step 10000: x = [2.999997854232788, 2.000002861022949], f(x) = 1.8189894035458565e-10
step 12000: x = [2.9999992847442627, 2.0000009536743164], f(x) = 1.6370904631912708e-11
step 14000: x = [2.999999761581421, 2.000000238418579], f(x) = 1.8189894035458565e-12
step 16000: x = [3.0, 2.0], f(x) = 0.0
step 18000: x = [3.0, 2.0], f(x) = 0.0
step 20000: x = [3.0, 2.0], f(x) = 0.0
标签：optimizer,0.0,torch,step,120,Pytorch,shape,ax 来源： https://www.cnblogs.com/Carrawayang/p/15819276.html