Hello everyone,

I am reading the paper “Bag of tricks for image classification with convolutional neural networks”. And I want to test the impact of “No bias decay”, which means only applying regularization to weights of convolution, and other parameters, including the biases and the parameters in BN layers are left unregularized.

I checked the code of convolution in PyTorch, but I found may there is no way to apply different regularization to conv’s weights and bias, respectively.

The code I found about the definition of Convolution,

class _ConvNd(Module):

```
__constants__ = ['stride', 'padding', 'dilation', 'groups', 'bias',
'padding_mode', 'output_padding', 'in_channels',
'out_channels', 'kernel_size']
def __init__(self, in_channels, out_channels, kernel_size, stride,
padding, dilation, transposed, output_padding,
groups, bias, padding_mode):
super(_ConvNd, self).__init__()
if in_channels % groups != 0:
raise ValueError('in_channels must be divisible by groups')
if out_channels % groups != 0:
raise ValueError('out_channels must be divisible by groups')
self.in_channels = in_channels
self.out_channels = out_channels
self.kernel_size = kernel_size
self.stride = stride
self.padding = padding
self.dilation = dilation
self.transposed = transposed
self.output_padding = output_padding
self.groups = groups
self.padding_mode = padding_mode
if transposed:
self.weight = Parameter(torch.Tensor(
in_channels, out_channels // groups, *kernel_size))
else:
self.weight = Parameter(torch.Tensor(
out_channels, in_channels // groups, *kernel_size))
if bias:
self.bias = Parameter(torch.Tensor(out_channels))
else:
self.register_parameter('bias', None)
self.reset_parameters()
```

If you have any idea about that, please shear something in this post. Thank you !