Comments (3)
I also have the same problem on cifar10, the loss will explode after some (about 12000) iterations.
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The prototxt actually works fine in my PC. In general, L-Softmax loss is indeed a little bit difficult to optimize since it defines a more difficult task than Softmax loss. However, the training prototxt file I provide is simply an example of how to use L-Softmax loss. If your network diverges, you should consider to change the parameters such as base, gamma, power, etc. to make lambda decrease more smoothly. There are more than one set of parameters that could achieve the performance in the paper. The last thing you can do if your network still diverges is to assign a small positive value to lambda_min, say 0.5. (it is a very rare case that you need to change lambda_min for CIFAR 10.) Also note that, you should use m=4 in L-Softmax loss if you want to reproduce the reported performance, while in the CIFAR10 example prototxt, m is set to 2. Thanks for these feedbacks.
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BTW, I just fix a bug about lambda_min. You guys should use the new version.
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Related Issues (20)
- hard to convergence HOT 4
- About A-Softmax HOT 21
- some typos in HOT 2
- Licensing HOT 3
- trian_accuracy decrease? HOT 1
- Computation of k value from eq. (6) HOT 2
- the deploy.prototxt of LargeMargin_Softmax_Loss HOT 5
- Check failed: target_blobs.size() == source_layer.blobs_size() (1 vs. 2) HOT 2
- Activation function problem HOT 1
- Pairs of testing
- Why `lambda = max(lambda_min,base*(1+gamma*iteration)^(-power)`? Any particular reason?
- Can I know which part of the paper do sign_x_ correspond to?
- void LargeMarginInnerProductLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
- train accuracy decrease HOT 1
- train mnist,loss is nan
- evaluate LargeMargin_Softmax_Loss on lfw
- L-softmax + center loss HOT 1
- L
- Angle margin
- Is the CIFAR10 dataset error rate given in the paper the result of a single model?
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