ValueError: Using a target size (torch.Size([64])) that is different to the input size (torch.Size([

ValueError                                Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_19156/279535578.py in <module>
     54         #计算损失函数
     55         label.data.fill_(1)
---> 56         error_real=criterion(output, label)
     57         error_real.backward() #辨别器的反向误差传播
     58         D_x=output.data.mean()
 
c:\users\25566\miniconda3\lib\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 = [], []

报错信息如上。报错信息说输入的尺寸和目标尺寸不同，导致的错误，所以解决方法是找到之前代码的output和label的尺寸赋值，改变如图上output和label的尺寸。

# 构造判别器
class ModelD(nn.Module):
    def __init__(self):
        super(ModelD,self).__init__()
        self.model=nn.Sequential() #序列化模块构造的神经网络
        self.model.add_module('conv1',nn.Conv2d(num_channels, num_features, 5, 2, 0, bias=False)) #卷积层
        self.model.add_module('relu1',nn.ReLU()) #激活函数使用了ReLu
        #self.model.add_module('relu1',nn.LeakyReLU(0.2, inplace = True)) #激活函数使用了leakyReLu，可以防止dead ReLu的问题
        
        #第二层卷积
        self.model.add_module('conv2',nn.Conv2d(num_features, num_features * 2, 5, 2, 0, bias=False))
        self.model.add_module('bnorm2',nn.BatchNorm2d(num_features * 2)) 
        self.model.add_module('linear1', nn.Linear(num_features * 2 * 4 * 4,   #全链接网络层
                                                   num_features))
        self.model.add_module('linear2', nn.Linear(num_features, 1)) #全链接网络层
        self.model.add_module('sigmoid',nn.Sigmoid())
    def forward(self,input):
        output = input
        # 对网络中的所有神经模块进行循环，并挑选出特定的模块linear1，将feature map展平
        for name, module in self.model.named_children():
            if name == 'linear1':
                output = output.view(-1, num_features * 2 * 4 * 4)
            output = module(output)
            output = output.squeeze(-1)
        return output

源代码里面是没有  output = output.squeeze(-1)的，添加这行代码即可解决问题。