PyTorch specify model parameters
I am trying to create a convolutional model in PyTorch where


one layer is fixed (initialized to prescribed values)
another layer is learned (but initial guess taken from prescribed values).


Here is a sample code for model definition:

import torch.nn as nn

class Net(nn.Module):
    def __init__(self, weights_fixed, weights_guess):
        super(Net, self).__init__()
        self.convL1 = nn.Conv1d(1, 3, 3, bias=False)
        self.convL1.weight = weights_fixed # I want to keep these weights fixed

        self.convL2 = nn.Conv1d(3, 1, 1, bias=False)
        self.convL1.weight = weights_guess # I want to learn these weights

    def forward(self, inp_batch):
        out1 = self.convL1(inp_batch)
        out2 = self.convL2(out1)

        return out2


and the sample use:

weights_fixed = ...
weights_guess = ...

model = Net(weights_fixed, weights_guess)

loss_fn = nn.CrossEntropyLoss()
optim = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9)

train_dataset = ... #define training set here

for (X, y) in train_dataset:
    optim.zero_grad()
    out = model(X)
    loss = loss_fn(out, y)
    loss.backward()
    optim.step() 


How can I make the weights 
weights_fixed - fixed and 
weights_guess - learnable?

My guess would be 
    weights_fixed = nn.Parameter(W1,requires_grad=False)
    weights_guess = nn.Parameter(W2,requires_grad=True)
where for the sake of completeness
    import numpy as np
    import torch

krnl = np.zeros((5,order+1))
krnl[:,0] = [ 0. , 1., 0. ]
krnl[:,1] = [-0.5, 0., 0.5]
krnl[:,2] = [ 1. ,-2., 1. ]
W1 = torch.tensor(krnl)

a = np.array((1.,2.,3.))
W2 = torch.tensor(a)


But I am utterly confused. Any suggestions or references would be greatly appreciated. Of course I went over PyTorch docs, but it did not add clarity to my understanding. 

Just wrap the learnable parameter with nn.Parameter (requires_grad=True is the default, no need to specify this), and have the fixed weight as a Tensor without nn.Parameter wrapper.

All nn.Parameter weights are automatically added to net.parameters(), so when you do training like optimizer = optim.SGD(net.parameters(), lr=0.01), the fixed weight will not be changed.

So basically this:

weights_fixed = W1
weights_guess = nn.Parameter(W2)

