yaringal
The loss might be nagative value
Thanks for your good jobs. But I have a question. I have transported your code into my project and it worked at that time. However after several steps the loss became nagative. And I found that it was the log_var item led to that. When I removed log_var item the loss would be all right. So I want to know if there is any better solution for that? Thanks again!
loss += K.sum(precision * (y_true - y_pred)**2. + log_var[0], -1)
@begeekmyfriend @joinssmith @celinew1221 @yaringal Do you have any progress?I meet the same problem,and I donot think we can remove log_var,because it is a way for measuring uncertainty,if it is removed,how do we self-learn weights for every single task in multilearning task. So have you found better solutions?
@begeekmyfriend @joinssmith @celinew1221 @yaringal Do you have any progress?I meet the same problem,and I donot think we can remove log_var,because it is a way for measuring uncertainty,if it is removed,how do we self-learn weights for every single task in multilearning task. So have you found better solutions?
During implementation, I think it makes sense to clip log_var at 0. That's what I did.
@celinew1221 Thank you for your helping.Would you mind tell me how to implement "clip log_var at 0“。
@celinew1221 Thank you for your helping.Would you mind tell me how to implement "clip log_var at 0“。
I'd just use torch.clamp(log_var, min=0)
@celinew1221 , But in paper "Geometric loss functions for camera pose regression with deep learning", the init value of log_var is -3.0. So I think it make no sense to clip the value at 0.
Well, that depends on the losses. It makes no sense to have negative loss in a cross entropy loss function tho. This is really a question for the original author. In my case, I don’t know what to do if not clip at 0 or use the absolute value.
On Oct 24, 2019, at 8:17 AM, ghoshaw [email protected] wrote:
@celinew1221 , But in paper "Geometric loss functions for camera pose regression with deep learning", the init value of log_var is -3.0. So I think it make no sense to clip the value at 0.
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Hi, @yaringal. I also meet this problem, and i want to know what's your understanding, thank you
If you have a Gaussian likelihood (equivalently, Euclidean loss) then the likelihood can take values larger than 1 (it's a density, not a probability mass). So the loss+penalty terms (i.e. negative log Gaussian density) can indeed become negative - this is not a bug.
