my apologies this may be a dumb question; I am not sure how to create a matrix of modules, should I just copy the parameters into a tensor of size (b×m×p)?
If I only use torch.tensor to create a new matrix of (b×m×p) dimension, then how to initialize and set the bias of that?
Here is what I tried, will back propagation be applied on output_layers later in training?
self.output_layers = torch.rand([8000, 128, 2], requires_grad=True)
torch.nn.init.normal_(self.output_layers, mean=0, std=5e-3)