我想优化conditon gan型号（生成器），错误：形状＆＃x27; [64，3，64，64]＆＃x27;对于2952192＆＃xff0c;

Question

这是原始的github来源：

我想重新运行该模型并使用另一个区域（CGAN）。

我想更改网络以生成多个标签并提高性能，但效果不佳。我需要有人帮助我！谢谢〜 错误信息如下。我认为问题是重塑问题，但我不知道该如何解决！需要一些帮助

 ```

输入属性 #number所有图像（81474）！！！

# Root directory for dataset
dataroot = "***"

# Number of workers for dataloader
workers = 2

# Batch size during training
batch_size = 64

# Spatial size of training images. All images will be resized to this
#   size using a transformer.
image_size = 64

#number of calsses lable 
n_class = 27

# Number of channels in the training images. For color images this is 3
nc = 3

# Size of z latent vector (i.e. size of generator input)
nz = 100

# Size of feature maps in generator （output)
ngf = 64

# Size of feature maps in discriminator
ndf = 64

# Number of training epochs
num_epochs = 5

# Learning rate for optimizers
lr = 0.0002

# Beta1 hyperparam for Adam optimizers
beta1 = 0.5

# Number of GPUs available. Use 0 for CPU mode.
ngpu = 1 ```


 ```def __init__(self, ngpu=1):
        super(Generator, self).__init__()
        self.label_emb = nn.Embedding(n_class, n_class)
        self.ngpu = ngpu
        self.main = nn.Sequential(
            # input is Z, going into a convolution
            nn.ConvTranspose2d( nz + n_class, ngf * 16, 4, 1, 0, bias=False),
            nn.BatchNorm2d(ngf * 16),
            nn.ReLU(True),
            # state size. (ngf*8) x 4 x 4
            nn.ConvTranspose2d(ngf * 16, ngf * 8, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ngf * 8),
            nn.ReLU(True),
            # state size. (ngf*4) x 8 x 8
            nn.ConvTranspose2d( ngf * 8, ngf * 4, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ngf * 4),
            nn.ReLU(True),
            # state size. (ngf*2) x 16 x 16
            nn.ConvTranspose2d( ngf * 4, ngf, 4, 4, 1, bias=False),
            nn.BatchNorm2d(ngf),
            nn.ReLU(True),
            # state size. (ngf) x 32 x 32
            nn.ConvTranspose2d(ngf, nc, 4, 2, 1, bias=False),
            nn.Tanh()
            # state size. (nc) x 64 x 64
        )
    def forward(self, noise_input, labels):
        # Concatenate label embedding and image to produce input
        #print(self.label_emb(labels).unsqueeze(2).unsqueeze(3).shape, noise_input.shape, labels.shape)
        gen_input = torch.cat((self.label_emb(labels).unsqueeze(2).unsqueeze(3), noise_input), 1)
        img = self.main(gen_input)
        img = img.view(img.size(0), *(nc, image_size, image_size))
        return img
    
netG = Generator(ngpu).to(device)```

<ipython-input-65-50e58bbfe414> in <module>
     37         noise = torch.randn(b_size, nz, 1, 1, device=device)
     38         # Generate fake image batch with G
---> 39         fake = netG(noise, fake_style_labels)
     40         label.fill_(fake_label)
     41         # Classify all fake batch with D

~/opt/anaconda3/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
   1100         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
   1101                 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1102             return forward_call(*input, **kwargs)
   1103         # Do not call functions when jit is used
   1104         full_backward_hooks, non_full_backward_hooks = [], []

<ipython-input-61-cb81d45887cf> in forward(self, noise_input, labels)
     31         gen_input = torch.cat((self.label_emb(labels).unsqueeze(2).unsqueeze(3), noise_input), 1)
     32         img = self.main(gen_input)
---> 33         img = img.view(img.size(0), *(nc, image_size, image_size))
     34         return img
     35 
RuntimeError: shape '[64, 3, 64, 64]' is invalid for input of size 2952192```