I am trying to use “mult” as learnable weights in this model, yet despite being in model.parameters() and requires_grad=True the weights aren’t being updated (and indeed mult.grad = None). What could have gone wrong?

```
class NeuralNetB(nn.Module):
def __init__(self):
super(NeuralNetB, self).__init__()
self.mult = nn.Parameter(torch.rand(3), requires_grad=True).to(device) #todo: why not learnable?
self.fc1 = nn.Linear(6, 64)
self.relu = nn.LeakyReLU()
self.fc2 = nn.Linear(64, 16)
self.fc3 = nn.Linear(16, 3)
self.drop = nn.Dropout(0.4)
self.fc4 = nn.Linear(30, 3)
def forward(self, x, tc):
print(self.mult)
x2 = x/tc.view(tc.shape[0],1,1) #get partial averages
x2 = x2*(self.mult*10).view(1,3,1) #multiply by weights
out = torch.sum(x2, dim=1) #sum them up
#print(self.mult)
out = self.fc1(out)
#out = self.relu(out)
out = self.fc2(out)
out = self.relu(out)
out = self.fc3(out)
#out = self.drop(out)
#out = self.fc4(out)
return out
```

Training:

```
y_pred = model(train_xB[v], train_count[v])
model.zero_grad()
loss = criterion(y_pred, train_yB[v])
loss.backward()
optimizer.step()
```