Custom loss function error: tensor does not have a grad_fn
Trying to utilize a custom loss function and getting error ‘RuntimeError: element 0 of tensors does not require grad and does not have a grad_fn’. Error occurs during loss.backward()
I’m aware that all computations must be done in tensors with ‘require_grad = True’. I’m having trouble implementing that as my code requires a nested for loop. I believe it could be the for loop. Is there a way to create an empty tensor and append it? Below is my code.
def Gaussian_Kernal(x, mu, sigma):
  p = (1./(math.sqrt(2. * math.pi * (sigma**2)))) * torch.exp((-1.) * (((Variable(x)**2) - mu)/(2. * (sigma**2))))
  return p

class MEE(torch.nn.Module):
  def __init__(self):
    super(MEE,self).__init__()

  def forward(self,output, target, mu, variance):

    error = torch.subtract(Variable(output),Variable(target))
  
    error_diff = []
    for i in range(0, error.size(0)):
      for j in range(0, error.size(0)):
        error_diff.append(error[i] - error[j])

    error_diff = torch.cat(error_diff)
    torch.tensor(error_diff,requires_grad=True)

    loss = (1./(target.size(0)**2)) * torch.sum(Gaussian_Kernal(Variable(error_diff), mu, variance*(2**0.5)))

    loss = Variable(loss)

    return loss


As long as you operate on Tensors and apply PyTorch functions and basic operators, it should work. Therefore no need to wrap your variables with torch.tensor or Variable. The latter has been being deprecated (since v0.4, I believe).

The Variable API has been deprecated: Variables are no longer
necessary to use autograd with tensors. Autograd automatically
supports Tensors with requires_grad set to True. PyTorch docs

I'm assuming output and target are tensors and mu and variance are reals and not tensors? Then, the first dimension of output and target would be the batch.
def Gaussian_Kernel(x, mu, sigma):
  p = (1./(math.sqrt(2. * math.pi * (sigma**2)))) * torch.exp((-1.) * (((x**2) - mu)/(2. * (sigma**2))))
  return p

class MEE(torch.nn.Module):
  def __init__(self):
    super(MEE, self).__init__()

  def forward(self, output, target, mu, variance):
    error = output - target

    error_diff = []
    for i in range(0, error.size(0)):
      for j in range(0, error.size(0)):
        error_diff.append(error[i] - error[j]) # Assuming that's the desired operation

    error_diff = torch.cat(error_diff)
    kernel = Gaussian_Kernel(error_diff, mu, variance*(2**0.5))
    loss = (1./(target.size(0)**2))*torch.sum(kernel)
    
    return loss

