I’m planning to use the Root Means Squared Log Error as a loss function for an image to image regression problem (these are not properly images but fields with variable norms). I did not find this function as part of the torch.nn.modules.loss implemented functions. What do you think would be the best way to implement Root Means Squared Log Error with PyTorch ?
One way of doing this is to create a
self.mse = nn.MSELoss()
def forward(self, pred, actual):
return torch.sqrt(self.mse(torch.log(pred + 1), torch.log(actual + 1)))
The equation I took as reference here is from a Kaggle discussion.
and using it just like any other loss function provided by pytorch.
pred = torch.tensor([600.], requires_grad=True)
actual = torch.tensor([1000.])
criterion = RMSLELoss()
rmsle = criterion(pred, actual)
# tensor(0.5102, grad_fn=<SqrtBackward>)
I hope this helps.
Thanks mate, indeed this is a direct way to do so.