```
class My_Model(nn.Module):
def __init__(self):
super(My_Model, self).__init__()
self.conv1 = nn.Conv2d(1, 8, 3,padding = (1,1)) #input_ch,out_ch,
self.conv2 = nn.Conv2d(8, 4, 3,padding=(1,1))
self.conv3 = nn.Conv2d(4, 2, 1)
self.conv4 = nn.Conv2d(4,1,3) #,padding = (1,1))
def forward(self, x):
x = F.relu(self.conv1(x))
x = F.relu(self.conv2(x))
x = F.relu(self.conv3(x))
bilinear = torch.nn.modules.upsampling.UpsamplingBilinear2d(scale_factor=2) #nn.UpsamplingBilinear2D(scale_factor = 2)
nearest = torch.nn.modules.upsampling.UpsamplingNearest2d(scale_factor = 2)
x_bilinear = bilinear(x)
x_nearest = nearest(x)
x = torch.stack((x_bilinear,x_nearest),dim=2)
x = x.view(34,4,34,34)
x = F.relu(self.conv4(x))
return x
```

This is the network I designed for some image inputs. Below is how I am training it.

```
model = My_Model()
model.float()
model.cuda()
model.train()
criterion = torch.nn.MSELoss(size_average=False)
optimizer = torch.optim.SGD(model.parameters(), lr=0.0002, momentum=0.9)
for loop over data ...
x_batch = torch.from_numpy(x_batch)
y_batch = torch.from_numpy(hr_batch)
x_batch = Variable(x_batch,requires_grad = True).cuda()
y_batch = Variable(y_batch,requires_grad = False).cuda()
model.zero_grad()
optimizer.zero_grad()
y_pred = model(x_batch)
print y_pred
loss = criterion(y_pred, y_batch)
print('loss = ', loss.data[0])
# Zero gradients, perform a backward pass, and update the weights.
loss.backward()
optimizer.step()
```

The input x_batch is 34 x 1 X 17 X 17 and y_batch is 34 X 1 X 32 X 32.

However while training, the y_pred ** always ** gives a zero tensor.

Please help.