optimizer.step() Not updating Model Weights/Parameters-CodePudding

I'm currently working on a solution via PyTorch. I'm not going to share the exact solution but I will provide code that reproduces the issue I'm having.

I have a model defined as follows:

class Net(nn.Module):
    def __init__(self):
        super(Net,self).__init__()
        self.fc1 = nn.Linear(10,4)

    def foward(self,x):
        return nn.functional.relu(self.fc1(x))

Then I create a instance: my_model = Net(). Next I create an Adam optimizer as such:

optim = Adam(my_model.parameters())

# create a random input
inputs = torch.tensor(np.array([1,1,1,1,1,2,2,2,2,2]),dtype=torch.float32,requires_grad=True)

# get the outputs
outputs = my_model(inputs)

# compute gradients / backprop via
outputs.backward(gradient=torch.tensor([1.,1.,1.,5.]))

# store parameters before optimizer step
before_step = list(my_model.parameters())[0].detach().numpy()

# update parameters via
optim.step()

# collect parameters again
after_step = list(my_model.parameters())[0].detach().numpy()

# Print if parameters are the same or not
print(np.array_equal(before_step,after_step)) # Prints True

I provided my models parameters to the Adam optimizer, so I'm not exactly sure why the parameters aren't updating. I know in most cases one uses a loss function, however I cannot do that in my case but I assumed if I specified model paramters to the optimizers, it would know to connect the two.

Anyone know why the parameters aren't getting updated?

CodePudding user response：

The problem is with detach (docs). As noted at the bottom:

Returned Tensor shares the same storage with the original one. In-place modifications on either of them will be seen, and may trigger errors in correctness checks

So that is exactly what's happening here. To correctly compare the parameters, you need to clone (docs) them to get a real copy.

list(my_model.parameters())[0].clone().detach().numpy()

On a side note, it can be helpful if you check the gradients after optim.step() with print(list(my_model.parameters())[0].grad) to check if the graph is intact. Also, don't forget to call optim.zero_grad().