Hi guys, I am trying to train my data with this model NN I have 7 inputs (features) with 1 output (label) I am trying to classifying the data and train and calculating the loss. I did not understand why this error exactly why its not matching!?

This is my NN model. 7 input(features) and 1 output (labels) hidden lyres are deffrent.

```
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.fc1 = nn.Linear(7, 100)
self.fc2 = nn.Linear(100, 80)
self.fc3 = nn.Linear(80, 50)
self.fc4 = nn.Linear(50, 30)
self.fc5 = nn.Linear(30, 10)
self.fc6 = nn.Linear(10, 1)
def forward(self, x):
x = self.fc1(x).clamp(min=0)
x = F.relu(self.fc2(x))
x = F.relu(self.fc3(x))
x = F.relu(self.fc4(x))
x = F.relu(self.fc5(x))
x = self.fc6(x)
return x
```

```
net = Net()
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(net.parameters(), lr=0.01, momentum=0.9)
```

```
for epoch in range(3):
iterations = 0
running_loss = 0
for i,(inputs,labels) in enumerate(train_loader):
iterations+=1
inputs = inputs.float()
labels = labels.long()
# Feed Forward
output = net(inputs)
# Loss Calculation
loss = criterion(output, labels)
running_loss = running_loss + loss.item()
_, prd = torch.max(output, dim = 1)
accuracy = (prd == labels).float().mean()
#accuracy = (labels).float().mean()
# Clear the gradient buffer (we don't want to accumulate gradients)
optimizer.zero_grad()
# Backpropagation
loss.backward()
# Weight Update: w <-- w - lr * gradient
optimizer.step()
#print("Epoch [{}][{}/{}], Loss: {:.3f}".format(epoch, i, len(train_loader), running_loss / iterations))
print("Epoch [{}][{}/{}], Loss: {:.3f}".format(epoch ,i , len(train_loader), running_loss))
```

So what i am getting an error is is telling that the either target labels or u out labels have different indices range.

```
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-38-7ccf84c3537e> in <module>
15 output = net(inputs)
16 # Loss Calculation
---> 17 loss = criterion(output, labels)
18
19
~\Anaconda3\lib\site-packages\torch\nn\modules\module.py in __call__(self, *input, **kwargs)
545 result = self._slow_forward(*input, **kwargs)
546 else:
--> 547 result = self.forward(*input, **kwargs)
548 for hook in self._forward_hooks.values():
549 hook_result = hook(self, input, result)
~\Anaconda3\lib\site-packages\torch\nn\modules\loss.py in forward(self, input, target)
914 def forward(self, input, target):
915 return F.cross_entropy(input, target, weight=self.weight,
--> 916 ignore_index=self.ignore_index, reduction=self.reduction)
917
918
~\Anaconda3\lib\site-packages\torch\nn\functional.py in cross_entropy(input, target, weight, size_average, ignore_index, reduce, reduction)
1993 if size_average is not None or reduce is not None:
1994 reduction = _Reduction.legacy_get_string(size_average, reduce)
-> 1995 return nll_loss(log_softmax(input, 1), target, weight, None, ignore_index, None, reduction)
1996
1997
~\Anaconda3\lib\site-packages\torch\nn\functional.py in nll_loss(input, target, weight, size_average, ignore_index, reduce, reduction)
1822 .format(input.size(0), target.size(0)))
1823 if dim == 2:
-> 1824 ret = torch._C._nn.nll_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index)
1825 elif dim == 4:
1826 ret = torch._C._nn.nll_loss2d(input, target, weight, _Reduction.get_enum(reduction), ignore_index)
RuntimeError: Assertion `cur_target >= 0 && cur_target < n_classes' failed. at C:\w\1\s\tmp_conda_3.7_055457\conda\conda-bld\pytorch_1565416617654\work\aten\src\THNN/generic/ClassNLLCriterion.c:94
```