Why use nn.Conv2D over nn.functional.conv2d?
Hi, I stumbled upon this question because I am trying to have control over how my convolutional weights are initialized. The API for both of those however seems different. For the former: torch.nn.functional.conv2d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1) For the latter: class torch.nn.Conv2d(in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True) So, my questions are the following: Thanks
Kalamaya
smth