Comments (3)
This phenomenon is normal. -log det (A)
can definitely get a negative value when A
is a positive definite matrix. You can also check the value of the det(A)
by printing it.
The sign of the loss does not matter, the gradient matters.
You can add -100
to the normal cross entropy loss, then your loss will be a negative value, but it will not change the training process and results.
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I have met the same problems
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“-log det (A) can definitely get a negative value when A is a positive definite matrix.” I can't understand this sentence. For example, A=Diag(0.1,0.1,0.1),A is a positive definite matrix, but det(A)=0.001, -log(det(A)) is a positive value.
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Related Issues (20)
- cholesky_cuda: For batch 0: U(1,1) is zero, singular U. HOT 4
- Use CRF as post-process in image segmentation HOT 4
- Sample weights for RMI loss HOT 4
- Comparison with the Potential Energy Loss on Gibbs distribution HOT 2
- RuntimeError: CUDA error: an illegal memory access was encountered HOT 2
- About setting of baseline HOT 2
- The intuition behind RMI loss HOT 2
- two classification tasks with output channel = 1 HOT 4
- How to weight some examples if loss may be negative HOT 1
- Benchmarking on Cityscapes HOT 3
- Effect of resizing image & mask without keeping width-height ratio in the data augmentation HOT 3
- Please do not raise issues here. This repo is archived and will no longer be maintained. Some 3rd party links maybe helpful in the content.
- Please do not raise issues here. This repo is archived and will no longer be maintained. Some 3rd party links maybe helpful in the content.
- Negative RMI loss HOT 1
- RMI HOT 1
- Training a DeepLabv3 model with output_stride=16, crop_size=513, and batch_size=8 on two 2080Ti GPUs HOT 3
- Hi! Can the RMI loss be used to measure the similarity of two natural images? HOT 1
- Not available to download ResNet101 pretrained model. HOT 3
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