Hi, I am working on a problem where I need to cache the model parameters (weights) for the last k iterations. In the next iteration, my model needs to use the parameters (randomly picked from the cached values) to compute the gradients.
I tried the following.
model = torch.nn.Sequential( torch.nn.Linear(1000, 100), torch.nn.ReLU(), torch.nn.Linear(100, 10), ) queue.push(model.parameters) delayed_params = queue.pop()
However, I am unable to make the
delayed_params for computing the gradients. Is there any way to solve this?