Hi, I have a question regarding a custom loss function. I am trying to design a loss function that suits my purpose (its related to adversarial attacks). I want to create a function that is based on BCELoss() so I started off with my own BCELoss() implementation:
class MlaLoss(nn.Module): def __init__(self, weight=None, size_average=True): super(MlaLoss, self).__init__() def forward(self, x, y): positive_loss = torch.clamp(torch.log(x), min=-100) negative_loss = torch.clamp(torch.log(1-x), min=-100) loss = -torch.mean(y * positive_loss + (1-y) * negative_loss) return loss
When I compare the loss to pytorch its BCELoss() I get the same values, the backpropagated gradients however deviate slightly, which degrades my attack performance significantly (if not drastically)
loss1 = nn.BCELoss() loss2 = MlaLoss() input1 = torch.rand(1000, 1000, requires_grad=True) input2 = torch.clone(input1).detach() input2.requires_grad = True target = torch.ones((1000,1000), requires_grad=False) cost1 = loss1(input1,target) cost2 = loss2(input2,target) cost1.backward() cost2.backward() print(torch.sum(cost1 - cost2)) print(torch.sum(input1.grad.data - input2.grad.data))
The outputs are as follows:
tensor(0., grad_fn=<SumBackward0>) tensor(-5.5235e-08)
How do I fix this deviation? Any suggestions or help would be very much appreciated!
Thanks in advance.