深度学习 PyTorch:Expected 4 维输入为 4 维权重 [64, 3, 7, 7]，但得到大小为 [32, 1728] 的二维输入

Question

我正在使用 PyTorch 的深度学习来做一些图像分类。每当我尝试训练我的模型时，forward 函数都会失败。有人可以解释一下输入大小错误的原因是什么以及如何解决这个问题吗？

这是我的模型的代码，以及我的训练损失和我的优化器：

model.fc = nn.Sequential(
        nn.Conv2d(1, 6, 9, padding=0),  # 64040
        nn.ReLU(),  #
        nn.AvgPool2d(2, stride=2),  # max 62020
        nn.Conv2d(6, 16, 11, padding=0),  # 161010
        nn.ReLU(),  # 161010
        nn.AvgPool2d(2, stride=2),  # 1655 = 400
        nn.Flatten(),
        nn.Linear(400, 200),
        nn.ReLU(),
        nn.Linear(200, 100),
        nn.ReLU(),
        nn.Linear(100, 3), 
        nn.LogSoftmax(dim=1))

criterion = nn.NLLLoss()
optimizer = optim.Adam(model.fc.parameters(), lr=0.003)
model.to(device)

这是验证函数：

def validation(model, val_dataloader, criterion):

val_loss = 0
accuracy = 0

for images, labels in iter(val_dataloader):

    images, labels = images.to('cuda'), labels.to('cuda')

    output = model.forward(images)
    val_loss += criterion(output, labels).item()

    probabilities = torch.exp(output)
    
    equality = (labels.data == probabilities.max(dim=1)[1])
    accuracy += equality.type(torch.FloatTensor).mean()

return val_loss, accuracy

最后，这是我的训练函数：

def train_classifier():
  epochs = 10
  steps = 0
  print_every = 40

  model.to('cuda')

  for e in range(epochs):
  
      model.train()

      running_loss = 0

      for images, labels in iter(train_dataloader):
        images = images.view(images.shape[0], -1)  #this flattens it?
        
        steps += 1

        images, labels = images.to('cuda'), labels.to('cuda')

        optimizer.zero_grad()

        # training 
        output = model.forward(images)
        loss = criterion(output, labels)
        loss.backward()
        optimizer.step()

        running_loss += loss.item()
  
        if steps % print_every == 0:
        
            model.eval()
        
            # Turn off gradients for validation, saves memory and computations
            with torch.no_grad():
                validation_loss, accuracy = validation(model, validate_loader, criterion)
    
            print("Epoch: {}/{}.. ".format(e+1, epochs),
                  "Training Loss: {:.3f}.. ".format(running_loss/print_every),
                  "Validation Loss: {:.3f}.. ".format(validation_loss/len(validate_loader)),
                  "Validation Accuracy: {:.3f}".format(accuracy/len(validate_loader)))
    
            running_loss = 0
            model.train()
                    
train_classifier()

错误代码：

---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-32-60a435d940e1> in <module>()
     49             model.train()
     50 
---> 51 train_classifier()

5 frames
/usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py in _conv_forward(self, input, weight, bias)
    438                             _pair(0), self.dilation, self.groups)
    439         return F.conv2d(input, weight, bias, self.stride,
--> 440                         self.padding, self.dilation, self.groups)
    441 
    442     def forward(self, input: Tensor) -> Tensor:

RuntimeError: Expected 4-dimensional input for 4-dimensional weight [64, 3, 7, 7], but got 2-dimensional input of size [32, 1728] instead

感谢任何帮助！谢谢！

Answer 1

你的网络既有 Conv2d 层也有完全连接的 Linear 层 - 这就是问题的根源：Conv2d 期望它的输入是 4D：batch-channel -高度宽度。另一方面，nn.Linear 作品的“扁平化”功能：批处理通道。因此，您需要“扁平化”您的数据，但不是在应用网络之前，而是在处理期间，您有一个 Flattening 层。

深度学习 PyTorch:Expected 4 维输入为 4 维权重 [64, 3, 7, 7]，但得到大小为 [32, 1728] 的二维输入

Deep learning with PyTorch:Expected 4-dimensional input for 4-dimensional weight [64, 3, 7, 7], but got 2-dimensional input of size [32, 1728] instead

python

machine-learning

deep-learning

pytorch