我收到了以下错误，
输入类型（torch.cuda.ByteTensor）和权重类型（torch.cuda.FloatTensor）应相同
以下是我的代码，
device = torch.device('cuda:0')trainData = torchvision.datasets.FashionMNIST('/content/', train=True, transform=None, target_transform=None, download=True)testData = torchvision.datasets.FashionMNIST('/content/', train=False, transform=None, target_transform=None, download=True)class Net(nn.Module): def __init__(self): super().__init__() ''' 网络结构： 输入 > (1)Conv2D > (2)MaxPool2D > (3)Conv2D > (4)MaxPool2D > (5)Conv2D > (6)MaxPool2D > (7)Linear > (8)LinearOut ''' # 创建卷积层 self.conv1 = nn.Conv2d(in_channels=CHANNELS, out_channels=32, kernel_size=3) self.conv2 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3) self.conv3 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3) self.flatten = None # 创建一个随机的虚拟样本来获取扁平化维度 x = torch.randn(CHANNELS, DIM, DIM).view(-1, CHANNELS, DIM, DIM) x = self.convs(x) # 创建线性层 self.fc1 = nn.Linear(self.flatten, 512) self.fc2 = nn.Linear(512, CLASSES) def convs(self, x): # 创建最大池化层 x = F.max_pool2d(F.relu(self.conv1(x)), kernel_size=(2, 2)) x = F.max_pool2d(F.relu(self.conv2(x)), kernel_size=(2, 2)) x = F.max_pool2d(F.relu(self.conv3(x)), kernel_size=(2, 2)) if not self.flatten: self.flatten = x[0].shape[0] * x[0].shape[1] * x[0].shape[2] return x # 前向传递 def forward(self, x): x = self.convs(x) x = x.view(-1, self.flatten) sm = F.relu(self.fc1(x)) x = F.softmax(self.fc2(sm), dim=1) return x, sm x_train, y_train = training_set x_train, y_train = x_train.to(device), y_train.to(device) optimizer = optim.Adam(net.parameters(), lr=LEARNING_RATE) loss_func = nn.MSELoss() loss_log = [] for epoch in range(EPOCHS): for i in tqdm(range(0, len(x_train), BATCH_SIZE)): x_batch = x_train[i:i+BATCH_SIZE].view(-1, CHANNELS, DIM, DIM).to(device) y_batch = y_train[i:i+BATCH_SIZE].to(device) net.zero_grad() output, sm = net(x_batch) loss = loss_func(output, y_batch.float()) loss.backward() optimizer.step() loss_log.append(loss) # print(f"Epoch : {epoch} || Loss : {loss}") return loss_logtrain_set = (trainData.train_data, trainData.train_labels)test_set = (testData.test_data, testData.test_labels)EPOCHS = 5LEARNING_RATE = 0.001BATCH_SIZE = 32net = Net().to(device)loss_log = train(net, train_set, EPOCHS, LEARNING_RATE, BATCH_SIZE)这是我收到的错误，
RuntimeError Traceback (most recent call last)<ipython-input-8-0db1a1b4e37d> in <module>() 5 net = Net().to(device) 6 ----> 7 loss_log = train(net, train_set, EPOCHS, LEARNING_RATE, BATCH_SIZE)6 frames<ipython-input-6-7de4a78e3736> in train(net, training_set, EPOCHS, LEARNING_RATE, BATCH_SIZE) 13 14 net.zero_grad()---> 15 output, sm = net(x_batch) 16 loss = loss_func(output, y_batch.float()) 17 loss.backward()/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs) 539 result = self._slow_forward(*input, **kwargs) 540 else:--> 541 result = self.forward(*input, **kwargs) 542 for hook in self._forward_hooks.values(): 543 hook_result = hook(self, input, result)<ipython-input-5-4fddc427892a> in forward(self, x) 41 # 前向传递 42 def forward(self, x):---> 43 x = self.convs(x) 44 x = x.view(-1, self.flatten) 45 sm = F.relu(self.fc1(x))<ipython-input-5-4fddc427892a> in convs(self, x) 31 32 # 创建最大池化层---> 33 x = F.max_pool2d(F.relu(self.conv1(x)), kernel_size=(2, 2)) 34 x = F.max_pool2d(F.relu(self.conv2(x)), kernel_size=(2, 2)) 35 x = F.max_pool2d(F.relu(self.conv3(x)), kernel_size=(2, 2))/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs) 539 result = self._slow_forward(*input, **kwargs) 540 else:--> 541 result = self.forward(*input, **kwargs) 542 for hook in self._forward_hooks.values(): 543 hook_result = hook(self, input, result)/usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py in forward(self, input) 343 344 def forward(self, input):--> 345 return self.conv2d_forward(input, self.weight) 346 347 class Conv3d(_ConvNd):/usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py in conv2d_forward(self, input, weight) 340 _pair(0), self.dilation, self.groups) 341 return F.conv2d(input, weight, self.bias, self.stride,--> 342 self.padding, self.dilation, self.groups) 343 344 def forward(self, input):RuntimeError: 输入类型（torch.cuda.ByteTensor）和权重类型（torch.cuda.FloatTensor）应相同我已经双重检查了我的神经网络和输入都已经在GPU上。我仍然收到这个错误，我不明白为什么！
请有人帮我解决这个错误。
回答：
将你的输入 x_batch 转换为浮点数。在通过模型之前使用 x_batch = x_batch.float()。
