(I am relatively new to PyTorch so sorry if it is too basic)

I want to access my convolutional and linear weights for regularization. However I have some PReLU parameters in my model that If I apply my regularization there, it will affect negatively my results.

Here is a toy model example:

```
#The Model
class TestModel(nn.Module):
def __init__(self):
super(TestModel, self).__init__()
self.input_conv= nn.Conv2d(3, 64, 5, padding=2)
self.i_activation= nn.PReLU(64)
self.downconv1= nn.Conv2d(64, 64, 3, padding=1)
self.d_activation1= nn.PReLU(64)
def forward(self, x):
x= self.input_conv(x)
x= self.i_activation(x)
x= self.downconv1(x)
x= self.d_activation1(x)
return x
#Instance of the model
model=TestModel()
```

Itterating with model.parameters does not work since PReLU stands in the way. Is there a kind of loop/function that I can use so that I can go through this? Thanks in advance