I am building a network that creates filters in a linear combination. But I don’t know if the approach is right. And it is also slow.

And I want to use the return value as weight.

How do you do it?

```
class Layer (torch.nn.Module):
def __init __ (self, in_ch, out_ch):
super (Layer, self) .__ init __ ()
self.out_ch = out_ch
self.in_ch = in_ch
self.weights = torch.nn.Parameter (torch.Tensor (out_ch, in_ch, 5))
def forward (self, x):
w = torch.empty (self.out_ch, self.in_ch, 3, 3) .to ('cuda')
for i in range (self.out_ch):
for j in range (self.in_ch):
w [i] [j] = x [0] .mul (self.weights [i] [j] [0]) + x [1] .mul (self.weights [i] [j] [1]) + x [2] .mul (self.weights [i] [j] [2]) \
+ x [3] .mul (self.weights [i] [j] [3]) + x [4] .mul (self.weights [i] [j] [4])
return w
class Net (nn.Module):
def __init__(self):
self.layer = Layer (3, 16)
def forward (self, x):
weight = self.layer(x) # x shape is (3, 3, 3)
x = F.conv2d (x, weight)
return x
```

- Computation Speed improvement
- How to update parameters with parameters