Question

我正在Google colab上训练pytorch神经网络，以对总共29个类的符号语言字母表进行分类。

我们一直在通过更改各种参数来修复代码，但是无论如何它还是行不通的。

    transform = transforms.Compose([

        #gray scale
        transforms.Grayscale(),

        #resize
        transforms.Resize((128,128)),

        #converting to tensor
        transforms.ToTensor(),

        #normalize
        transforms.Normalize( (0.1307,), (0.3081,)),
    ])

    data_dir = 'data/train/asl_alphabet_train'

    #dataset
    full_dataset = datasets.ImageFolder(root=data_dir, transform=transform)

    #train & test 
    train_size = int(0.8 * len(full_dataset))
    test_size = len(full_dataset) - train_size

    #splitting
    train_dataset, test_dataset = torch.utils.data.random_split(full_dataset, [train_size, test_size])

    trainloader = torch.utils.data.DataLoader(train_dataset , batch_size = 4, shuffle = True )
    testloader = torch.utils.data.DataLoader(test_dataset , batch_size = 4, shuffle = False )

    #neural net architecture
    Net(
  (conv1): Conv2d(1, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (conv2): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (conv3): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (fc1): Linear(in_features=32768, out_features=128, bias=True)
  (fc2): Linear(in_features=128, out_features=29, bias=True)
  (dropout): Dropout(p=0.5)
   )

    loss_fn = nn.CrossEntropyLoss()
    #optimizer
    opt = optim.SGD(model.parameters(), lr=0.01)
    def train(model, train_loader, optimizer, loss_fn, epoch, device):
        #telling pytorch that training mode is on
        model.train()
        loss_epoch_arr = []

        #epochs
        for e in range(epoch):

            # bach_no, data, target
            for batch_idx, (data, target) in enumerate(train_loader):

                #moving to GPU
                #data, target = data.to(device), target.to(device)

                #Making gradints zero
                optimizer.zero_grad()

                #generating output
                output = model(data)

                #calculating loss
                loss = loss_fn(output, target)

                #backward propagation
                loss.backward()

                #stepping optimizer
                optimizer.step()

                #printing at each 10th epoch
                if batch_idx % 10 == 0:
                    print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                        epoch, batch_idx * len(data), len(train_loader.dataset),
                        100. * batch_idx / len(train_loader), loss.item()))


                #de-allocating memory
                del data,target,output
                #torch.cuda.empty_cache()

            #appending values
            loss_epoch_arr.append(loss.item())

        #plotting loss
        plt.plot(loss_epoch_arr)
        plt.show()

    train(model, trainloader , opt, loss_fn, 10, device)

ValueError：预期输入batch_size（1）匹配目标batch_size （4）。

我们是pytorch的初学者，试图找出问题所在。

Answer 1

此错误的最可能原因与nn.Linear函数中的in_features值有关您尚未提供完整的代码。

一种检查方法是在转发功能中添加以下几行（在x.view之前：

    print('x_shape:',x.shape)

结果的格式为 [a，b，c，d] 。 in_features值应等于 b * c * d

如何解决“ ValueError：预期输入batch_size（1）匹配目标batch_size（4）”的问题？

1 个答案: