RuntimeError：标量类型为Long的预期对象，但参数＃2'target'的标量类型为Float

问题描述

在计算神经网络的损失时遇到问题。我不确定为什么程序期望长对象，因为我所有的张量都是浮点形式。我查看了具有类似错误的线程，解决方案是将Tensors转换为浮点数而不是长整型，但这在我的情况下不起作用，因为我的所有数据在传递到网络时都已经处于浮点数形式。

这是我的代码：

# Dataloader
from torch.utils.data import Dataset,DataLoader

class LoadInfo(Dataset):    
    def __init__(self,prediction,indicator):  
        self.prediction = prediction
        self.indicator = indicator
    def __len__(self):
        return len(self.prediction)
    def __getitem__(self,idx):
        data = torch.tensor(self.indicator.iloc[idx,:],dtype=torch.float)
        data = torch.unsqueeze(data,0)
        label = torch.tensor(self.prediction.iloc[idx,dtype=torch.float)
        sample = {'data': data,'label': label} 
        return sample

# Trainloader
test_train = LoadInfo(train_label,train_indicators)
trainloader = DataLoader(test_train,batch_size=64,shuffle=True,num_workers=1,pin_memory=True) 

# The Network
class NetDense2(nn.Module):

    def __init__(self):
        super(NetDense2,self).__init__()
        self.rnn1 = nn.RNN(11,100,3)  
        self.rnn2 = nn.RNN(100,500,3)  
        self.fc1 = nn.Linear(500,100)  
        self.fc2 = nn.Linear(100,20)
        self.fc3 = nn.Linear(20,3)

    def forward(self,x):
        x1,h1 = self.rnn1(x)
        x2,h2 = self.rnn2(x1)
        x = F.relu(self.fc1(x2))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

# Allocate / Transfer to GPU      
dense2 = NetDense2()
dense2.cuda()

# Optimizer
import torch.optim as optim
criterion = nn.CrossEntropyLoss()                                 # specify the loss function
optimizer = optim.SGD(dense2.parameters(),lr=0.001,momentum=0.9,weight_decay=0.001)

# Training
dense2.train()
loss_memory = []
for epoch in range(50):  # loop over the dataset multiple times
    running_loss = 0.0
    for i,samp in enumerate(trainloader):
        # get the inputs
        ins = samp['data']
        targets = samp['label']
        tmp = []
        tmp = torch.squeeze(targets.float())
        ins,targets = ins.cuda(),tmp.cuda()
        # zero the parameter gradients
        optimizer.zero_grad()
        # forward + backward + optimize
        outputs = dense2(ins)
        loss = criterion(outputs,targets)     # The loss
        loss.backward()
        optimizer.step()
        # keep track of loss
        running_loss += loss.data.item()

我在“损失=准则（输出，目标）”行中从上面得到了错误

解决方法

根据pytorch webpage上的文档和官方示例，传递给nn.CrossEntropyLoss()的目标应为torch.long格式

# official example
import torch
import torch.nn as nn
loss = nn.CrossEntropyLoss()
input = torch.randn(3,5,requires_grad=True)
target = torch.empty(3,dtype=torch.long).random_(5) 

# if you will replace the dtype=torch.float,you will get error

output = loss(input,target)
output.backward()

将代码中的这一行更新为

label = torch.tensor(self.prediction.iloc[idx,:],dtype=torch.long) #updated torch.float to torch.long