cifar validation loss decrease than increase after learning rate change

[Sirius083]

Hello, I have one question when training denseNet: the validation loss get a sharp decrease than increase after learning rate changed from 0.1 to 0.01
I trained the densenet (depth_40_k_12) on cifar100 by tensorflow implementation
https://github.com/YixuanLi/densenet-tensorflow
I just modifed the code follow your data augmentation step (subtract channel mean, then divide by std)
However the validation loss seems werid(In figure)I have following two questions

(1) Do you met the same problem when training cifar100 dataset(or it may be some tensorflow implementation error)
(2) Did your validation loss include L2 loss part?
Since the validation error seems no problem here(25.53%, 1.1% higher than that in the paper)
Thanks in advance

[liuzhuang13]

1. We didn't meet the same problem training on CIFAR-100. I think on SVHN similar things happened, but the increase is not as significant as this.
2. Our validation loss does not include L2 loss.

Thanks

[liuzhuang13]

Did you use data augmentation? (Padding, cropping and flipping on input image). If you didn't, it could overfit on training set and the test loss could increase.

[Sirius083]

Thanks for replying, I am using the exact same data augmentation as your code(one floating point channel mean and channel standard deviation, 4-pixel padding, flipping). I am further compare the code between pytorch and tensorflow to check the difference. Thanks a lot.

[Sirius083]

Did you use data augmentation? (Padding, cropping and flipping on input image). If you didn't, it could overfit on training set and the test loss could increase.

Thanks for your reply, I finally found that the tensorflow implementation did not include L2 norm on batch normalization parameters, after I adding that, the error rate decrease 0.5%, which decreased a lot. Thanks a lot.