这是我第一次使用 GAN，我面临着一个关于鉴别器反复优于生成器的问题。我正在尝试重现本文中的PA模型，并且正在研究这个略有不同的实现来帮助我。
我读过很多关于 GAN 如何工作的论文，也遵循了一些教程来更好地理解它们。此外，我读过关于如何克服主要不稳定性的文章，但我找不到克服这种行为的方法。
在我的环境中，我正在使用PyTorch和BCELoss()。在DCGAN PyTorch教程之后，我使用以下训练循环：
criterion = nn.BCELoss()
train_d = False
# Discriminator true
optim_d.zero_grad()
disc_train_real = target.to(device)
batch_size = disc_train_real.size(0)
label = torch.full((batch_size,), 1, device=device).cuda()
output_d = discriminator(disc_train_real).view(-1)
loss_d_real = criterion(output_d, label).cuda()
if lossT:
loss_d_real *= 2
if loss_d_real.item() > 0.3:
loss_d_real.backward()
train_d = True
D_x = output_d.mean().item()
# Discriminator false
output_g = generator(image)
output_d = discriminator(output_g.detach()).view(-1)
label.fill_(0)
loss_d_fake = criterion(output_d, label).cuda()
D_G_z1 = output_d.mean().item()
if lossT:
loss_d_fake *= 2
loss_d = loss_d_real + loss_d_fake
if loss_d_fake.item() > 0.3:
loss_d_fake.backward()
train_d = True
if train_d:
optim_d.step()
# Generator
label.fill_(1)
output_d = discriminator(output_g).view(-1)
loss_g = criterion(output_d, label).cuda()
D_G_z2 = output_d.mean().item()
if lossT:
loss_g *= 2
loss_g.backward()
optim_g.step()
而且，经过一段时间的安顿，一切似乎都正常：
Epoch 1/5 - Step: 1900/9338 Loss G: 3.057388 Loss D: 0.214545 D(x): 0.940985 D(G(z)): 0.114064 / 0.114064
Time for the last step: 51.55 s Epoch ETA: 01:04:13
Epoch 1/5 - Step: 2000/9338 Loss G: 2.984724 Loss D: 0.222931 D(x): 0.879338 D(G(z)): 0.159163 / 0.159163
Time for the last step: 52.68 s Epoch ETA: 01:03:24
Epoch 1/5 - Step: 2100/9338 Loss G: 2.824713 Loss D: 0.241953 D(x): 0.905837 D(G(z)): 0.110231 / 0.110231
Time for the last step: 50.91 s Epoch ETA: 01:02:29
Epoch 1/5 - Step: 2200/9338 Loss G: 2.807455 Loss D: 0.252808 D(x): 0.908131 D(G(z)): 0.218515 / 0.218515
Time for the last step: 51.72 s Epoch ETA: 01:01:37
Epoch 1/5 - Step: 2300/9338 Loss G: 2.470529 Loss D: 0.569696 D(x): 0.620966 D(G(z)): 0.512615 / 0.350175
Time for the last step: 51.96 s Epoch ETA: 01:00:46
Epoch 1/5 - Step: 2400/9338 Loss G: 2.148863 Loss D: 1.071563 D(x): 0.809529 D(G(z)): 0.114487 / 0.114487
Time for the last step: 51.59 s Epoch ETA: 00:59:53
Epoch 1/5 - Step: 2500/9338 Loss G: 2.016863 Loss D: 0.904711 D(x): 0.621433 D(G(z)): 0.440721 / 0.435932
Time for the last step: 52.03 s Epoch ETA: 00:59:02
Epoch 1/5 - Step: 2600/9338 Loss G: 2.495639 Loss D: 0.949308 D(x): 0.671085 D(G(z)): 0.557924 / 0.420826
Time for the last step: 52.66 s Epoch ETA: 00:58:12
Epoch 1/5 - Step: 2700/9338 Loss G: 2.519842 Loss D: 0.798667 D(x): 0.775738 D(G(z)): 0.246357 / 0.265839
Time for the last step: 51.20 s Epoch ETA: 00:57:19
Epoch 1/5 - Step: 2800/9338 Loss G: 2.545630 Loss D: 0.756449 D(x): 0.895455 D(G(z)): 0.403628 / 0.301851
Time for the last step: 51.88 s Epoch ETA: 00:56:27
Epoch 1/5 - Step: 2900/9338 Loss G: 2.458109 Loss D: 0.653513 D(x): 0.820105 D(G(z)): 0.379199 / 0.103250
Time for the last step: 53.50 s Epoch ETA: 00:55:39
Epoch 1/5 - Step: 3000/9338 Loss G: 2.030103 Loss D: 0.948208 D(x): 0.445385 D(G(z)): 0.303225 / 0.263652
Time for the last step: 51.57 s Epoch ETA: 00:54:47
Epoch 1/5 - Step: 3100/9338 Loss G: 1.721604 Loss D: 0.949721 D(x): 0.365646 D(G(z)): 0.090072 / 0.232912
Time for the last step: 52.19 s Epoch ETA: 00:53:55
Epoch 1/5 - Step: 3200/9338 Loss G: 1.438854 Loss D: 1.142182 D(x): 0.768163 D(G(z)): 0.321164 / 0.237878
Time for the last step: 50.79 s Epoch ETA: 00:53:01
Epoch 1/5 - Step: 3300/9338 Loss G: 1.924418 Loss D: 0.923860 D(x): 0.729981 D(G(z)): 0.354812 / 0.318090
Time for the last step: 52.59 s Epoch ETA: 00:52:11
也就是说，生成器上的梯度较高，并在一段时间后开始减小，同时鉴别器上的梯度上升。至于损失，生成器下降，而鉴别器上升。如果与教程相比，我想这是可以接受的。
这是我的第一个问题：我注意到在教程中(通常(随着D_G_z1的上升，D_G_z2减少(反之亦然(，而在我的示例中，这种情况发生得更少。这只是一个巧合还是我做错了什么？
鉴于此，我让训练程序继续进行，但现在我注意到了这一点：
Epoch 3/5 - Step: 1100/9338 Loss G: 4.071329 Loss D: 0.031608 D(x): 0.999969 D(G(z)): 0.024329 / 0.024329
Time for the last step: 51.41 s Epoch ETA: 01:11:24
Epoch 3/5 - Step: 1200/9338 Loss G: 3.883331 Loss D: 0.036354 D(x): 0.999993 D(G(z)): 0.043874 / 0.043874
Time for the last step: 51.63 s Epoch ETA: 01:10:29
Epoch 3/5 - Step: 1300/9338 Loss G: 3.468963 Loss D: 0.054542 D(x): 0.999972 D(G(z)): 0.050145 / 0.050145
Time for the last step: 52.47 s Epoch ETA: 01:09:40
Epoch 3/5 - Step: 1400/9338 Loss G: 3.504971 Loss D: 0.053683 D(x): 0.999972 D(G(z)): 0.052180 / 0.052180
Time for the last step: 50.75 s Epoch ETA: 01:08:41
Epoch 3/5 - Step: 1500/9338 Loss G: 3.437765 Loss D: 0.056286 D(x): 0.999941 D(G(z)): 0.058839 / 0.058839
Time for the last step: 52.20 s Epoch ETA: 01:07:50
Epoch 3/5 - Step: 1600/9338 Loss G: 3.369209 Loss D: 0.062133 D(x): 0.955688 D(G(z)): 0.058773 / 0.058773
Time for the last step: 51.05 s Epoch ETA: 01:06:54
Epoch 3/5 - Step: 1700/9338 Loss G: 3.290109 Loss D: 0.065704 D(x): 0.999975 D(G(z)): 0.056583 / 0.056583
Time for the last step: 51.27 s Epoch ETA: 01:06:00
Epoch 3/5 - Step: 1800/9338 Loss G: 3.286248 Loss D: 0.067969 D(x): 0.993238 D(G(z)): 0.063815 / 0.063815
Time for the last step: 52.28 s Epoch ETA: 01:05:09
Epoch 3/5 - Step: 1900/9338 Loss G: 3.263996 Loss D: 0.065335 D(x): 0.980270 D(G(z)): 0.037717 / 0.037717
Time for the last step: 51.59 s Epoch ETA: 01:04:16
Epoch 3/5 - Step: 2000/9338 Loss G: 3.293503 Loss D: 0.065291 D(x): 0.999873 D(G(z)): 0.070188 / 0.070188
Time for the last step: 51.85 s Epoch ETA: 01:03:25
Epoch 3/5 - Step: 2100/9338 Loss G: 3.184164 Loss D: 0.070931 D(x): 0.999971 D(G(z)): 0.059657 / 0.059657
Time for the last step: 52.14 s Epoch ETA: 01:02:34
Epoch 3/5 - Step: 2200/9338 Loss G: 3.116310 Loss D: 0.080597 D(x): 0.999850 D(G(z)): 0.074931 / 0.074931
Time for the last step: 51.85 s Epoch ETA: 01:01:42
Epoch 3/5 - Step: 2300/9338 Loss G: 3.142180 Loss D: 0.073999 D(x): 0.995546 D(G(z)): 0.054752 / 0.054752
Time for the last step: 51.76 s Epoch ETA: 01:00:50
Epoch 3/5 - Step: 2400/9338 Loss G: 3.185711 Loss D: 0.072601 D(x): 0.999992 D(G(z)): 0.076053 / 0.076053
Time for the last step: 50.53 s Epoch ETA: 00:59:54
Epoch 3/5 - Step: 2500/9338 Loss G: 3.027437 Loss D: 0.083906 D(x): 0.997390 D(G(z)): 0.082501 / 0.082501
Time for the last step: 52.06 s Epoch ETA: 00:59:03
Epoch 3/5 - Step: 2600/9338 Loss G: 3.052374 Loss D: 0.085030 D(x): 0.999924 D(G(z)): 0.073295 / 0.073295
Time for the last step: 52.37 s Epoch ETA: 00:58:12
不仅D(x)再次增加并且几乎坚持到一个，而且D_G_z1和D_G_z2总是显示相同的值。此外，从损失来看，很明显鉴别器的表现优于生成器。这种行为在剩下的时代和下一个时代一直持续到训练结束。
因此，我的第二个问题：这正常吗？如果不是，我在程序中做错了什么？如何获得更稳定的训练？
编辑：我尝试按照建议使用MSELoss()训练网络，这是输出：
Epoch 1/1 - Step: 100/9338 Loss G: 0.800785 Loss D: 0.404525 D(x): 0.844653 D(G(z)): 0.030439 / 0.016316
Time for the last step: 55.22 s Epoch ETA: 01:25:01
Epoch 1/1 - Step: 200/9338 Loss G: 1.196659 Loss D: 0.014051 D(x): 0.999970 D(G(z)): 0.006543 / 0.006500
Time for the last step: 51.41 s Epoch ETA: 01:21:11
Epoch 1/1 - Step: 300/9338 Loss G: 1.197319 Loss D: 0.000806 D(x): 0.999431 D(G(z)): 0.004821 / 0.004724
Time for the last step: 51.79 s Epoch ETA: 01:19:32
Epoch 1/1 - Step: 400/9338 Loss G: 1.198960 Loss D: 0.000720 D(x): 0.999612 D(G(z)): 0.000000 / 0.000000
Time for the last step: 51.47 s Epoch ETA: 01:18:09
Epoch 1/1 - Step: 500/9338 Loss G: 1.212810 Loss D: 0.000021 D(x): 0.999938 D(G(z)): 0.000000 / 0.000000
Time for the last step: 52.18 s Epoch ETA: 01:17:11
Epoch 1/1 - Step: 600/9338 Loss G: 1.216168 Loss D: 0.000000 D(x): 0.999945 D(G(z)): 0.000000 / 0.000000
Time for the last step: 51.24 s Epoch ETA: 01:16:02
Epoch 1/1 - Step: 700/9338 Loss G: 1.212301 Loss D: 0.000000 D(x): 0.999970 D(G(z)): 0.000000 / 0.000000
Time for the last step: 51.61 s Epoch ETA: 01:15:02
Epoch 1/1 - Step: 800/9338 Loss G: 1.214397 Loss D: 0.000005 D(x): 0.999973 D(G(z)): 0.000000 / 0.000000
Time for the last step: 51.58 s Epoch ETA: 01:14:04
Epoch 1/1 - Step: 900/9338 Loss G: 1.212016 Loss D: 0.000003 D(x): 0.999932 D(G(z)): 0.000000 / 0.000000
Time for the last step: 52.20 s Epoch ETA: 01:13:13
Epoch 1/1 - Step: 1000/9338 Loss G: 1.215162 Loss D: 0.000000 D(x): 0.999988 D(G(z)): 0.000000 / 0.000000
Time for the last step: 52.28 s Epoch ETA: 01:12:23
Epoch 1/1 - Step: 1100/9338 Loss G: 1.216291 Loss D: 0.000000 D(x): 0.999983 D(G(z)): 0.000000 / 0.000000
Time for the last step: 51.78 s Epoch ETA: 01:11:28
Epoch 1/1 - Step: 1200/9338 Loss G: 1.215526 Loss D: 0.000000 D(x): 0.999978 D(G(z)): 0.000000 / 0.000000
Time for the last step: 51.88 s Epoch ETA: 01:10:35
可以看出，情况变得更糟。此外，再次阅读EnhanceNet论文，第4.2.4节(对抗训练(指出，使用的对抗损失函数是一个BCELoss()，因为我希望解决我用MSELoss()得到的梯度消失问题。
解释 GAN 损失有点黑艺术，因为实际损失值
问题 1：区分器/生成器优势之间的摆动频率主要取决于几个因素(根据我的经验(：学习率和批量大小，这将影响传播的损失。使用的特定损失指标将影响D&G网络训练方式的差异。EnhanceNet论文(用于基线(和教程也使用了均方误差损失 - 您正在使用二进制交叉熵损失，它将改变网络收敛的速率。我不是专家，所以这里有一个很好的链接，指向Rohan Varma的文章，解释了损失函数之间的区别。会很好奇，当您更改损失函数时，您的网络行为是否有所不同 - 尝试一下并更新问题？
问题2：随着时间的推移，D和G损失都应该稳定到一个值，但是很难判断它们是否已经收敛于强大的性能，或者它们是否由于模式崩溃/梯度递减之类的东西而收敛(Jonathan Hui对训练GANs问题的解释(。我发现的最好的方法是实际检查生成图像的横截面，然后目视检查输出或在生成的图像集中使用某种感知指标(SSIM，PSNR，PIQ等(。
您可能会发现其他一些有用的线索对查找和以下内容很有用：
这篇文章有几个关于解释GAN损失的相当好的指示。
Ian Goodfellow的NIPS2016教程也有一些关于如何平衡D&G训练的坚实想法。