Hello everyone!
I am working with PyTorch and implementing a semantic segmentation method. The loss function used is a combination of 6 loss terms, i.e. L= L1+L2+L3+L4+L5+L6. Now my requirement is to use trainable weights with these 6 loss terms, which means L= w1L1+w2L2+w3L3+w4L4+w5L5+w6L6.
Now in order to get trainable weights, I am using torch.autograd() class, means
w1 = torch.randn(x.size(),require_grad=True), like this I am doing for all the 6 weights.
Does this method takes care of backpropagation ? or Is this the correct way to use the trainable weights in PyTorch?