我尝试了很多次来修复这个问题，我也使用了functional.py中的示例代码，然后得到了相同的“loss”值。我该如何修复这个问题？
我的库如下：
import matplotlib.pyplot as pltimport torchimport torch.nn as nnimport numpy as npimport matplotlibimport pandas as pdfrom torch.autograd import Variablefrom torch.utils.data import DataLoader,TensorDatasetfrom sklearn.model_selection import train_test_splitimport warningsimport osimport torchvisionimport torchvision.datasets as dsetsimport torchvision.transforms as transformstrain=pd.read_csv("train.csv",dtype=np.float32) targets_numpy = train.label.valuesfeatures_numpy = train.loc[:,train.columns != "label"].values/255 # normalization features_train, features_test, targets_train, targets_test = train_test_split(features_numpy, targets_numpy,test_size = 0.2, random_state = 42)featuresTrain=torch.from_numpy(features_train)targetsTrain=torch.from_numpy(targets_train) featuresTest=torch.from_numpy(features_test)targetsTest=torch.from_numpy(targets_test) batch_size=100n_iterations=10000num_epochs=n_iterations/(len(features_train)/batch_size)num_epochs=int(num_epochs) train=torch.utils.data.TensorDataset(featuresTrain,targetsTrain) test=torch.utils.data.TensorDataset(featuresTest,targetsTest)print(type(train)) train_loader=DataLoader(train,batch_size=batch_size,shuffle=False)test_loader=DataLoader(test,batch_size=batch_size,shuffle=False)print(type(train_loader)) plt.imshow(features_numpy[226].reshape(28,28))plt.axis("off")plt.title(str(targets_numpy[226]))plt.show()class ANNModel(nn.Module): def __init__(self,input_dim,hidden_dim,output_dim): super(ANNModel,self).__init__() self.fc1=nn.Linear(input_dim,hidden_dim) self.relu1=nn.ReLU() self.fc2=nn.Linear(hidden_dim,hidden_dim) self.tanh2=nn.Tanh() self.fc4=nn.Linear(hidden_dim,output_dim) def forward (self,x): #forward ile elde edilen layer lar bağlanır out=self.fc1(x) out=self.relu1(out) out=self.fc2(out) out=self.tanh2(out) out=self.fc4(out) return out input_dim=28*28hidden_dim=150 output_dim=10 model=ANNModel(input_dim,hidden_dim,output_dim) error=nn.CrossEntropyLoss() learning_rate=0.02optimizer=torch.optim.SGD(model.parameters(),lr=learning_rate) count=0loss_list=[]iteration_list=[]accuracy_list = []for epoch in range(num_epochs): for i,(images,labels) in enumerate(train_loader): train=Variable(images.view(-1,28*28)) labels=Variable(labels) #print(labels) #print(outputs) optimizer.zero_grad() #forward propagation outputs=model(train) #outputs=torch.randn(784,10,requires_grad=True) ##labels=torch.randn(784,10).softmax(dim=1) loss=error(outputs,labels) loss.backward() optimizer.step() count+=1 if count % 50 == 0: correct=0 total=0 for images,labels in test_loader: test=Variable(images.view(-1,28*28)) outputs=model(test) predicted=torch.max(outputs.data,1)[1] #mantık??? total+= len(labels) correct+=(predicted==labels).sum() accuracy=100 *correct/float(total) loss_list.append(loss.data) iteration_list.append(count) accuracy_list.append(accuracy) if count % 500 == 0: print('Iteration: {} Loss: {} Accuracy: {} %'.format(count, loss.data, accuracy))错误信息如下：
---------------------------------------------------------------------------RuntimeError Traceback (most recent call last)<ipython-input-9-9e53988ad250> in <module>() 26 #outputs=torch.randn(784,10,requires_grad=True) 27 ##labels=torch.randn(784,10).softmax(dim=1)---> 28 loss=error(outputs,labels) 29 30 2 frames/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py in cross_entropy(input, target, weight, size_average, ignore_index, reduce, reduction, label_smoothing) 2844 if size_average is not None or reduce is not None: 2845 reduction = _Reduction.legacy_get_string(size_average, reduce)-> 2846 return torch._C._nn.cross_entropy_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index, label_smoothing) 2847 2848 RuntimeError: expected scalar type Long but found Float回答：
看起来张量“labels”的数据类型是FloatTensor。然而，nn.CrossEntropyLoss期望目标类型为LongTensor。这意味着你应该检查“labels”的类型。如果是这种情况，你应该使用以下代码将“labels”的数据类型从FloatTensor转换为LongTensor：
loss=error(outputs,labels.long())