Adam Optimizer not Updating Values
I am trying to use Adam optimizer to obtain certain values outside of a neural network. My technique wasn't working so I created a simple example to see if it works:
a = np.array([[0.0,1.0,2.0,3.0,4.0], [0.0,1.0,2.0,3.0,4.0]])
b = np.array([[0.1,0.2,0.0,0.0,0.0], [0.0,0.5,0.0,0.0,0.0]])
a = torch.from_numpy(a)
b = torch.from_numpy(b)
a.requires_grad = True
b.requires_grad = True
optimizer = torch.optim.Adam(
        [b],
        lr=0.01,
        weight_decay=0.001
    )

iterations = 200
for i in range(iterations ):
    loss = torch.sqrt(((a.detach() - b.detach()) ** 2).sum(1)).mean()
    loss.requires_grad = True 
    
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

    if i % 10 == 0:
        print(b)
        print(&quot;loss:&quot;, loss)

My intuition was b should get close to a as much as possible to reduce loss. But I see no change in any of the values of b and loss stays exactly the same. What am I missing here? Thanks.

You are detaching b, meaning the gradient won't flow all the way to b when backpropagating, i.e. b won't change! Additionally, you don't need to state requires_grad = True on the loss, as this is done automatically since one of the operands has the requires_grad flag on.
loss = torch.sqrt(((a.detach() - b) ** 2).sum(1)).mean()

