Apply torch functions to weight parameters

weight_ops.py

import torch
import torch.nn as nn
from torch.autograd import Variable

affine = nn.Linear(10, 10)
# A linear mapping to a random vector... just for quick demo purposes
x = Variable(torch.randn(100, 10))
y = Variable(torch.randn(100, 10))

weird_loss = torch.mean(torch.exp(affine.weight))
mse_loss = nn.MSELoss()
pred_loss = mse_loss(affine(x), y)
net_loss = pred_loss + weird_loss
net_loss.backward()
# should work just fine!