Can someone help me understand why the weights are not updating?

```
unet = Unet()
optimizer = torch.optim.Adam(unet.parameters(), lr=0.1)
loss_fn = torch.nn.BCELoss()
input = torch.randn(32, 1, 64, 64, 64 , requires_grad=True)
target = torch.randn(32, 1, 64, 64, 64, requires_grad=False)
optimizer.zero_grad()
y_pred = unet(input)
y = target[: , : , 20:44, 20:44, 20:44]
loss = loss_fn(y_pred, y)
print(unet.conv1.weight.data)
loss.backward()
optimizer.step()
print(unet.conv1.weight.data)
```

The init of the model is define like this :

```
class Unet(nn.Module):
def __init__(self):
super(Unet, self).__init__()
# Down hill1
self.conv1 = nn.Conv3d(1, 2, kernel_size=3, stride=1)
self.conv2 = nn.Conv3d(2, 2, kernel_size=3, stride=1)
# Down hill2
self.conv3 = nn.Conv3d(2, 4, kernel_size=3, stride=1)
self.conv4 = nn.Conv3d(4, 4, kernel_size=3, stride=1)
#bottom
self.convbottom1 = nn.Conv3d(4, 8, kernel_size=3, stride=1)
self.convbottom2 = nn.Conv3d(8, 8, kernel_size=3, stride=1)
#up hill1
self.upConv0 = nn.Conv3d(8, 4, kernel_size=3, stride=1)
self.upConv1 = nn.Conv3d(4, 4, kernel_size=3, stride=1)
self.upConv2 = nn.Conv3d(4, 2, kernel_size=3, stride=1)
#up hill2
self.upConv3 = nn.Conv3d(2, 2, kernel_size=3, stride=1)
self.upConv4 = nn.Conv3d(2, 1, kernel_size=1, stride=1)
self.mp = nn.MaxPool3d(kernel_size=3, stride=2, padding=1)
self.output1 = 0
self.output2 = 0
self.output3 = 0
self.output4 = 0
self.output5 = 0
self.output6 = 0
self.output7 = 0
self.output8 = 0
self.output9 = 0
self.output10 = 0
self.output11 = 0
self.output12 = 0
self.output13 = 0
self.output14 = 0
```

The forward pass follows an approach of:

```
def forward(self, input):
def forward(self, input):
# Use U-net Theory to Update the filters.
# Example Approach...
self.output1 = F.relu(self.conv1(input))
self.output2 = F.relu(self.conv2(self.output1))
self.output3 = self.mp(self.output2)
self.output4 = F.relu(self.conv3(self.output3))
self.output5 = F.relu(self.conv4(self.output4))
self.output6 = self.mp(self.output5)
self.output7 = F.relu(self.convbottom1(self.output6))
self.output8 = F.relu(self.convbottom2(self.output7))
self.output9 = F.interpolate(self.output8, scale_factor=2, mode='trilinear')
self.output10 = F.relu(self.upConv0(self.output9))
self.output11 = F.relu(self.upConv1(self.output10))
self.output12 = F.interpolate(self.output11, scale_factor=2, mode='trilinear')
self.output13 = F.relu(self.upConv2(self.output12))
self.output14 = F.relu(self.upConv3(self.output13))
return F.relu(self.upConv4(self.output14))
```