RuntimeError: mat1和mat2形状不能相乘(32x400和600x120)
RuntimeError: mat1和mat2形状不能相乘(32x400和600x120)
提问于 2022-11-07 02:11:20
下面我有下面的CNN，我得到了以下错误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6，32x32张带有10个标签的图像。如果我正确理解，x = F.relu(self.fc1(x))的大小输入应该是600x200，但实际上是32x400。我迷失的地方是我需要改变(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()我试图从其他具有类似错误的帖子中获得解决方案，但无法修复我的代码。我也尝试过添加torch.nn.AdaptiveMaxPool2d，但是我认为我没有正确地使用它，也不确定我是否真的需要使用它。
回答 1
Stack Overflow用户
发布于 2022-11-07 02:31:23
如果用平均池进行下采样，则最好使用“相同”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页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/74341292
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