我就废话不多说了，直接上代码吧！
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
|
import torch
import torch.nn as nn
from torch.autograd import Variable
import numpy as np
import matplotlib.pyplot as plt
torch.manual_seed(1)
np.random.seed(1)
BATCH_SIZE = 64
LR_G = 0.0001
LR_D = 0.0001
N_IDEAS = 5
ART_COMPONENTS = 15
PAINT_POINTS = np.vstack([np.linspace(-1,1,ART_COMPONENTS) for _ in range(BATCH_SIZE)])
def artist_works():
a = np.random.uniform(1,2,size=BATCH_SIZE)[:,np.newaxis]
paintings = a*np.power(PAINT_POINTS,2) + (a-1)
paintings = torch.from_numpy(paintings).float()
return Variable(paintings)
G = nn.Sequential(
nn.Linear(N_IDEAS,128),
nn.ReLU(),
nn.Linear(128,ART_COMPONENTS),
)
D = nn.Sequential(
nn.Linear(ART_COMPONENTS,128),
nn.ReLU(),
nn.Linear(128,1),
nn.Sigmoid(),
)
opt_D = torch.optim.Adam(D.parameters(),lr=LR_D)
opt_G = torch.optim.Adam(G.parameters(),lr=LR_G)
plt.ion()
for step in range(10000):
artist_paintings = artist_works()
G_ideas = Variable(torch.randn(BATCH_SIZE,N_IDEAS))
G_paintings = G(G_ideas)
prob_artist0 = D(artist_paintings)
prob_artist1 = D(G_paintings)
D_loss = - torch.mean(torch.log(prob_artist0) + torch.log(1-prob_artist1))
G_loss = torch.mean(torch.log(1 - prob_artist1))
opt_D.zero_grad()
D_loss.backward(retain_variables=True)
opt_D.step()
opt_G.zero_grad()
G_loss.backward()
opt_G.step()
if step % 50 == 0:
plt.cla()
plt.plot(PAINT_POINTS[0],G_paintings.data.numpy()[0],c='#4ad631',lw=3,label='Generated painting',)
plt.plot(PAINT_POINTS[0],2 * np.power(PAINT_POINTS[0], 2) + 1,c='#74BCFF',lw=3,label='upper bound',)
plt.plot(PAINT_POINTS[0],1 * np.power(PAINT_POINTS[0], 2) + 0,c='#FF9359',lw=3,label='lower bound',)
plt.text(-.5,2.3,'D accuracy=%.2f (0.5 for D to converge)' % prob_artist0.data.numpy().mean(), fontdict={'size':15})
plt.text(-.5, 2, 'D score= %.2f (-1.38 for G to converge)' % -D_loss.data.numpy(), fontdict={'size': 15})
plt.ylim((0,3))
plt.legend(loc='upper right', fontsize=12)
plt.draw()
plt.pause(0.01)
plt.ioff()
plt.show()
|
以上这篇pytorch GAN生成对抗网络实例就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持服务器之家。
原文链接：https://blog.csdn.net/pureszgd/article/details/75095332