我在尝试使用PyTorch在CIFAR10数据集上创建一个逻辑模型。然而，我遇到了以下错误：
ValueError: Expected input batch_size (900) to match target batch_size (300).
我认为发生的问题是3乘以100等于300。所以可能是RGB图像的3个通道导致的，但我不知道如何解决这个问题。
这些是我的超参数设置：
batch_size = 100learning_rate = 0.001# Other constantsinput_size = 32*32num_classes = 10在这里，我将数据分为训练、验证和测试数据集。
transform_train = transforms.Compose([transforms.Resize((32,32)), transforms.RandomHorizontalFlip(), transforms.RandomRotation(10), transforms.RandomAffine(0, shear=10, scale=(0.8,1.2)), transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) ])transform = transforms.Compose([transforms.Resize((32,32)), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) ])training_dataset = CIFAR10(root='D:\PyTorch\cifar-10-python', train=True, download=True, transform=transform_train)train_ds, val_ds = random_split(training_dataset, [40000, 10000])test_ds = CIFAR10(root='D:\PyTorch\cifar-10-python', train=False, download=True, transform=transform)train_loader = DataLoader(train_ds, batch_size=100, shuffle=True)val_loader = DataLoader(val_ds, batch_size = 100, shuffle = False)test_loader = DataLoader(test_ds, batch_size = 100, shuffle=False)这是我的模型：
class CifarModel(nn.Module): def __init__(self): super().__init__() self.linear = nn.Linear(input_size, num_classes) def forward(self, xb): xb = xb.view(-1, 32*32) #xb = xb.reshape(-1, 784) print(xb.shape) out = self.linear(xb) return out def training_step(self, batch): images, labels = batch out = self(images) # Generate predictions loss = F.cross_entropy(out, labels) # Calculate loss return loss def validation_step(self, batch): images, labels = batch out = self(images) # Generate predictions loss = F.cross_entropy(out, labels) # Calculate loss acc = accuracy(out, labels) # Calculate accuracy return {'val_loss': loss.detach(), 'val_acc': acc.detach()} def validation_epoch_end(self, outputs): batch_losses = [x['val_loss'] for x in outputs] epoch_loss = torch.stack(batch_losses).mean() # Combine losses batch_accs = [x['val_acc'] for x in outputs] epoch_acc = torch.stack(batch_accs).mean() # Combine accuracies return {'val_loss': epoch_loss.item(), 'val_acc': epoch_acc.item()} def epoch_end(self, epoch, result): print("Epoch [{}], val_loss: {:.4f}, val_acc: {:.4f}".format(epoch, result['val_loss'], result['val_acc']))model = CifarModel()def accuracy(outputs, labels): _, preds = torch.max(outputs, dim=1) return torch.tensor(torch.sum(preds == labels).item() / len(preds))def evaluate(model, val_loader): outputs = [model.validation_step(batch) for batch in val_loader] return model.validation_epoch_end(outputs)def fit(epochs, lr, model, train_loader, val_loader, opt_func=torch.optim.SGD): history = [] optimizer = opt_func(model.parameters(), lr) for epoch in range(epochs): # Training Phase for batch in train_loader: loss = model.training_step(batch) loss.backward() optimizer.step() optimizer.zero_grad() # Validation phase result = evaluate(model, val_loader) model.epoch_end(epoch, result) history.append(result) return historyevaluate(model, val_loader)当我运行evaluate函数时，遇到了以下错误：
torch.Size([900, 1024])---------------------------------------------------------------------------ValueError Traceback (most recent call last)<ipython-input-23-3621eab8de1a> in <module> 21 history.append(result) 22 return history---> 23 evaluate(model, val_loader)<ipython-input-23-3621eab8de1a> in evaluate(model, val_loader) 3 return torch.tensor(torch.sum(preds == labels).item() / len(preds)) 4 def evaluate(model, val_loader):----> 5 outputs = [model.validation_step(batch) for batch in val_loader] 6 return model.validation_epoch_end(outputs) 7 <ipython-input-23-3621eab8de1a> in <listcomp>(.0) 3 return torch.tensor(torch.sum(preds == labels).item() / len(preds)) 4 def evaluate(model, val_loader):----> 5 outputs = [model.validation_step(batch) for batch in val_loader] 6 return model.validation_epoch_end(outputs) 7 <ipython-input-22-c9e17d21eaff> in validation_step(self, batch) 19 images, labels = batch 20 out = self(images) # Generate predictions---> 21 loss = F.cross_entropy(out, labels) # Calculate loss 22 acc = accuracy(out, labels) # Calculate accuracy 23 return {'val_loss': loss.detach(), 'val_acc': acc.detach()}~\Anaconda3\lib\site-packages\torch\nn\functional.py in cross_entropy(input, target, weight, size_average, ignore_index, reduce, reduction) 2019 if size_average is not None or reduce is not None: 2020 reduction = _Reduction.legacy_get_string(size_average, reduce)-> 2021 return nll_loss(log_softmax(input, 1), target, weight, None, ignore_index, None, reduction) 2022 2023 ~\Anaconda3\lib\site-packages\torch\nn\functional.py in nll_loss(input, target, weight, size_average, ignore_index, reduce, reduction) 1834 if input.size(0) != target.size(0): 1835 raise ValueError('Expected input batch_size ({}) to match target batch_size ({}).'-> 1836 .format(input.size(0), target.size(0))) 1837 if dim == 2: 1838 ret = torch._C._nn.nll_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index)ValueError: Expected input batch_size (900) to match target batch_size (300).回答：
我发现的一个问题是这行代码：
xb = xb.view(-1, 32*32) 这里你表示输入图像只有一个通道。换句话说，就是灰度图。请修改它以反映通道数（RGB）：
xb = xb.view(-1, 32*32*3) 