我目前正在开发一个能够分类猫、狗以及非猫非狗的其他物体的神经网络。我的程序出现了以下无法解决的错误：

” File “/home/johann/Schreibtisch/NN_v0.01/classification.py”, line 146, in train(epoch)File “/home/johann/Schreibtisch/NN_v0.01/classification.py”, line 109, in trainloss = criterion(out, target)File “/home/johann/.local/lib/python3.8/site-packages/torch/nn/modules/module.py”, line 889, in _call_implresult = self.forward(*input, **kwargs)File “/home/johann/.local/lib/python3.8/site-packages/torch/nn/modules/loss.py”, line 1047, in forwardreturn F.cross_entropy(input, target, weight=self.weight,File “/home/johann/.local/lib/python3.8/site-packages/torch/nn/functional.py”, line 2693, in cross_entropyreturn nll_loss(log_softmax(input, 1), target, weight, None, ignore_index, None, reduction)File “/home/johann/.local/lib/python3.8/site-packages/torch/nn/functional.py”, line 2388, in nll_lossret = torch._C._nn.nll_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index)RuntimeError: 1D target tensor expected, multi-target not supported”

代码如下：

import osfrom torchvision import transformsfrom PIL import Imagefrom os import listdirimport randomimport torch.optim as optimfrom torch.autograd import Variableimport torch.nn.functional as Fimport torch.nn as nnimport matplotlib.pyplot as pltfrom tqdm import tqdmnormalize = transforms.Normalize(    mean = [0.485, 0.456, 0.406],    std = [0.229, 0.224, 0.225])transform = transforms.Compose([     transforms.Resize(256),     transforms.CenterCrop(256),     transforms.ToTensor(), normalize])train_data_list = []target_list = []train_data = []waited = Falsefiles = listdir('catsanddogs/train/')for i in range(len(listdir('catsanddogs/train/'))):    if len(train_data) == 58 and not waited:        waited = True        continue    f = random.choice(files)    files.remove(f)    img = Image.open("catsanddogs/train/" + f)    img_tensor = transform(img)    train_data_list.append(img_tensor)    isSomething = 0    isCat = 1 if 'cat' in f else 0    isDog = 1 if 'dog' in f else 0    if isDog == 0 and isCat == 0:        isSomething = 1    target = [isCat, isDog, isSomething] #, isSomthing    target_list.append(target)    if len(train_data_list) >= 256:        train_data.append((torch.stack(train_data_list), target_list))        train_data_list = []        target_list = []        print('Loaded batch ', len(train_data), 'of ', int(len(listdir('catsanddogs/train/')) / 64))        print('Percentage Done: ', 100 * len(train_data) / int(len(listdir('catsanddogs/train/')) / 64), '%')        if len(train_data) > 2:            breakclass Netz(nn.Module):    def __init__(self):        super(Netz, self).__init__()        self.conv1 = nn.Conv2d(3, 6, kernel_size=(5, 5))        self.conv2 = nn.Conv2d(6, 12, kernel_size=(5, 5))        self.conv3 = nn.Conv2d(12, 18, kernel_size=(5, 5))        self.conv4 = nn.Conv2d(18, 24, kernel_size=(5, 5))        self.fc1 = nn.Linear(3456, 1000)        self.fc2 = nn.Linear(1000, 3)    def forward(self, x):       x = self.conv1(x)       x = F.max_pool2d(x, 2)       x = F.relu(x)       x = self.conv2(x)       x = F.max_pool2d(x, 2)       x = F.relu(x)       x = self.conv3(x)       x = F.max_pool2d(x, 2)       x = F.relu(x)       x = self.conv4(x)       x = F.max_pool2d(x, 2)       x = F.relu(x)       x = x.view(-1, 3456)       x = F.relu(self.fc1(x))       x = self.fc2(x)       return F.softmax(x, dim=1)model = Netz()#if os.path.isfile('catdognetz.pt'):    #model = torch.load('catdognetz.pt')optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)def count_parameters(model):  return sum(p.numel() for p in model.parameters() if p.requires_grad)count_parameters(model)train_losses = []train_accu = []def train(epoch):    print('\nEpoch : %d' % epoch)    model.train()    running_loss = 0    correct = 0    total = 0    for data, target in tqdm(train_data):        target = torch.Tensor(target)        data = Variable(data)        target = Variable(target)        optimizer.zero_grad()        out = model(data)        criterion = nn.CrossEntropyLoss()        loss = criterion(out, target)        loss.backward()        optimizer.step()        running_loss += loss.item()        predicted = out.max(1)        total += target.size(0)        correct += predicted.eq(target).sum().item()    train_loss = running_loss / len(train_data)    accu = 100. * float(correct) / total    train_accu.append(accu)    train_losses.append(train_loss)    print('Train Loss: %.3f | Accuracy: %.3f' % (train_loss, accu))def test():    model.eval()    file = listdir('catsanddogs/test/')    f = random.choice(file)    img = Image.open('catsanddogs/test/' + f)    img_eval_tensor = transform(img)    img_eval_tensor.unsqueeze_(0)    data = Variable(img_eval_tensor)    out = model(data)    what = "It's a cat"    print(out.data.max(1, keepdim=True)[1].cpu().numpy()[0])    if out.data.max(1, keepdim=True)[1].cpu().numpy()[0] != 0:        what = "It's a Dog"    if out.data.max(1, keepdim=True)[1].cpu().numpy()[0] != 0 and out.data.max(1, keepdim=True)[1].cpu().numpy()[0] != 1:        what = "Neither"    print(what)    img.show()    #x = input('')epochs = 10for epoch in range(1, epochs + 1):    train(epoch)    #test()    torch.save(model, 'catdognetz.pt')plt.plot(train_accu,'-o')plt.xlabel('epoch')plt.ylabel('accuracy')plt.legend(['Kurve: Training'])plt.title('Train Accuracy')plt.show()plt.plot(train_losses,'-o')plt.xlabel('epoch')plt.ylabel('loss bzw. Verlust')plt.legend(['Kruve : Training'])plt.title('Train Loss')plt.show()

回答：