GAN训练生成的图像不清晰且无法辨认

Question

训练的CGAN，采用cifar10数据集，经过20次训练后已经能出现图像，但是图像不清晰，而且图像看不出是什么东西。

如图所示，请问是训练次数不够还是说训练方法有错？
这是生成器和判别器的代码

class DCDiscriminator(nn.Module)
   https://img-mid.csdnimg.cn/release/static/image/mid/ask/855452777176119.png "#left")
 def __init__(self)
        super(DCDiscriminator, self).__init__()
        self.dis=nn.Sequential(
            nn.Conv2d(3+10,64,4,2,1,bias=False),
            nn.LeakyReLU(0.2,inplace=True),

            nn.Conv2d(64,64*2,4,2,1,bias=False),
            nn.BatchNorm2d(64 * 2),
            nn.LeakyReLU(0.2, inplace=True),

            nn.Conv2d(64 * 2, 64 * 4, 4, 2, 1, bias=False),
            nn.BatchNorm2d(64 * 4),
            nn.LeakyReLU(0.2, inplace=True),

            nn.Conv2d(64 * 4, 64 * 8, 4, 2, 1, bias=False),
            nn.BatchNorm2d(64 * 8),
            nn.LeakyReLU(0.2, inplace=True),

            nn.Conv2d(64 * 8, 1, 4, 1, 0, bias=False),
            nn.Sigmoid()
        )
    def forward(self, x)
        return self.dis(x).view(-1, 1).squeeze(1)

class DCGenerator(nn.Module)
    def __init__(self)
        super(DCGenerator, self).__init__()
        self.gen=nn.Sequential(
            nn.ConvTranspose2d(100+10, 64 * 8, 4, 1, 0, bias=False),
            nn.BatchNorm2d(64 * 8),
            nn.ReLU(True),
            # state size. (ngf*8) x 4 x 4
            nn.ConvTranspose2d(64 * 8, 64 * 4, 4, 2, 1, bias=False),
            nn.BatchNorm2d(64 * 4),
            nn.ReLU(True),

            nn.ConvTranspose2d(64 * 4, 64 * 2, 4, 2, 1, bias=False),
            nn.BatchNorm2d(64 * 2),
            nn.ReLU(True),

            nn.ConvTranspose2d(64 * 2, 64, 4, 2, 1, bias=False),
            nn.BatchNorm2d(64),
            nn.ReLU(True),

            nn.ConvTranspose2d(64, 3, 4, 2, 1, bias=False),
            nn.Tanh()
        )

    def forward(self,x)
        return self.gen(x)

batch_size=128，learning_rate=0.0002

Answer 1

loss函数弄错了吧