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. 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