I am facing some issues with training a network with multiple outputs .It is an image classification problem.Every image belongs to one of ten subclasses from two classes.Thus the

labels vector is a tensor of size [60,2,10](Note Here 60 corresponds to the Batch_size)

.For this model i am implementing the LeNet Architecture with two output tensors and backpropagating the combined loss.

Here is my model

```
class LeNet(nn.Module):
def __init__(self):
super(LeNet,self).__init__()
self.cnn_model=nn.Sequential(nn.Conv2d(1,6,5),
nn.ReLU(),
nn.AvgPool2d(2,stride=2),
nn.Conv2d(6,16,5),
nn.ReLU(),
nn.AvgPool2d(2,stride=2))
self.fc_model=nn.Sequential(
nn.Linear(400,120),
nn.LeakyReLU(),
nn.Linear(120,84),
nn.LeakyReLU(),
nn.Linear(84,10))
def forward(self,x):
x1=self.cnn_model(x)
x1=x1.view(x1.size(0),-1)
x1=self.fc_model(x1)
x2=self.cnn_model(x)
x2=x2.view(x2.size(0),-1)
x2=self.fc_model(x2)
return x1,x2
```

```
net=LeNet().to(device)
import torch.optim as optim
loss_fn=nn.CrossEntropyLoss()
opt=optim.Adam(net.parameters())
```

This is my training loop

```
%%time
loss_arr=[]
loss_epoch_arr=[]
epochs =25
for epoch in range(epochs):
for i,data in enumerate(train_loader,0):
inputs,labels=data
inputs,labels=inputs.to(device),labels.to(device)
opt.zero_grad()
out1,out2=net(inputs)
loss1=loss_fn(out1,labels[:,0,:].long())
loss2=loss_fn(out2,labels[:,1,:].long())
loss=loss1+loss2
loss.backward()
opt.step()
loss_arr.append(loss.item())
loss_epoch_arr.append(loss.item())
print("Epoch:%d/%d,Test_Acc: %0.2f,Train_Acc: %0.2f"%(epoch,epochs,evaluation(testloader),evaluation(trainloader)))
plt.plot(loss_epoch_arr)
plt.show()
```

But i am getting the following error

Thanks i in advance if you have any suggestions please provide some code as i am a beginner