Hey all,
I am doing some stuff for uni, I have to build a basic neural net based of the dataset provided to us which is a series of opcodes and the net has to determine which is malware and which is clean based on a predefined tag.
There I am getting is when I run the code to start the train net I get a not implmeneted error. from googling most people reckon that it is an indenting issue but I have typed and retpyed it to check for this and I am still getting the error.
model.train() 
for data in dataset_train:
    optimizer.zero_grad()
    output = model(data)
    loss = criterion(output)
    loss.backward()
    optimizer.step()
    train_loss += loss.item() * data.size(0)

model.eval() 
for data in dataset_test:
    output = model(data)
    loss = criterion(output, data)
    valid_loss += loss.item() * data.size(0)

train_loss = train_loss / len(train_loader.sampler)
valid_loss = valid_loss / len(valid_loader.sampler)

print('Epoch: {} \tTraining Loss: {:.6f} \tValidation Loss: {:.6f}'.format(
    epoch + 1,
    train_loss,
))

---------------------------------------------------------------------------
NotImplementedError                       Traceback (most recent call last)
<ipython-input-32-302975be982c> in <module>
      2 for data in sampleset:
      3     optimizer.zero_grad()
----> 4     output = model(data)
      5     loss = criterion(output)
      6     loss.backward()

D:\Anaconda3\lib\site-packages\torch\nn\modules\module.py in _call_impl(self, *input, **kwargs)
    725             result = self._slow_forward(*input, **kwargs)
    726         else:
--> 727             result = self.forward(*input, **kwargs)
    728         for hook in itertools.chain(
    729                 _global_forward_hooks.values(),

D:\Anaconda3\lib\site-packages\torch\nn\modules\module.py in _forward_unimplemented(self, *input)
    173         registered hooks while the latter silently ignores them.
    174     """
--> 175     raise NotImplementedError
    176 
    177 

NotImplementedError: 

As I said I have a feeling that maybe I have not imported the data correctly, the code to pull in the opcodes into a list is done for us already we just have to prep it for the neural net. I done that use the code below.
sampleset = []
sampleset.extend(malware)
sampleset.extend(clean)

sampleset = torch.LongTensor(sampleset)

dataset_train = torch.utils.data.DataLoader(sampleset, batch_size=1000, shuffle=True)
dataset_test = torch.utils.data.DataLoader(sampleset, batch_size=500, shuffle=True)

Any advice or pointers would be appreciated