I am trying to calculate grads with a custom list of inputs.
Here is the code snippet:
etas = []
for param in cv_params:
eta = 2 * F.sigmoid(Variable(torch.zeros(1)))
param.grad = param.grad * eta
etas.append(eta)
flat_params = []
for param in cv_params:
flat_params.append(param.grad.view(-1))
flat_params = torch.cat(flat_params, 0)
var_loss = (flat_params**2).mean()
var_grads = torch.autograd.grad(outputs=var_loss, inputs=etas, create_graph=True)
torch.autograd.backward(etas, var_grads)
but it gives me this error:
RuntimeError: One of the differentiated Variables does not require grad
Even though the inputs are differentiable parameters present in the computation graph of var_loss.