运行时错误：mat1 和 mat2 形状无法相乘（32x400 和 600x120）

Question

我有以下 CNN，但出现以下错误

 RuntimeError: mat1 and mat2 shapes cannot be multiplied (32x400 and 600x120)

。我使用的是 CIFAR10 数据集，该数据集总共包含 6,000 张 32x32 图像，带有 10 个标签。如果我理解正确的话，输入到

x = F.relu(self.fc1(x))

的大小应该是600x200，但输入实际上是32x400。我迷失的是我需要更改（或计算）的部分。

transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.5,), (0.5,))
])

batch_size = 32

cifar10 = torchvision.datasets.CIFAR10(root='./data', download=True, transform=torchvision.transforms.ToTensor())
pivot = 40000
cifar10 = sorted(cifar10, key=lambda x: x[1])
train_set = torch.utils.data.Subset(cifar10, range(pivot))
val_set = torch.utils.data.Subset(cifar10, range(pivot, len(cifar10)))
train_loader = DataLoader(train_set, batch_size=batch_size, shuffle=True)
val_loader = DataLoader(val_set, batch_size=batch_size, shuffle=True)

class Network(nn.Module):
    def __init__(self):
        super(Network, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(600, 120)
        self.fc2 = nn.Linear(120, 2)
        self.fc3 = nn.Linear(2, 10)
        self.flatten = nn.Flatten(1)
    
    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = self.flatten(x)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

model = Network()

我尝试了其他具有类似错误的帖子中的解决方案，但无法修复我的代码。我还尝试添加

torch.nn.AdaptiveMaxPool2d

但我认为我使用得不正确，并且不确定我是否真的需要使用它。

Answer 1

如果通过池化进行下采样，最好使用“相同”的填充卷积 2d。

class Network(nn.Module):
    def __init__(self):
        super(Network, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, kernel_size=5, padding=2)
        self.pool = nn.MaxPool2d(2, 2) # downsample / 2
        self.conv2 = nn.Conv2d(6, 16, kernel_size=5, padding=2)
        self.fc1 = nn.Linear(8*8*16, 120)
        self.fc2 = nn.Linear(120, 2)
        self.fc3 = nn.Linear(2, 10)
        self.flatten = nn.Flatten(1)
    
    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = self.flatten(x)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x