Closed koalazf99 closed 5 years ago
Can you redo this but with 20 instead of 5 power iterations:
https://github.com/jhjacobsen/invertible-resnet/blob/master/CIFAR_main.py#L34
@koalazf99 did increasing nr. of power iterations solve the problem for you?
@koalazf99 did increasing nr. of power iterations solve the problem for you?
So sorry that I forgot to give a quick response.
Increasing the nr. of power iterations seems to be not useful in CIFAR dataset.
I later found that it may be the problem of loading the pretrained model and initializing the iResNet.
Anyway, the problem is solved now and thanks a lot for your help!
I use your command script to run a classification model and meet these 2 issues.
When the model hasn’t been trained, I test its inverse function. And the error of a (3x32x32 sized) picture is only about 0.001 when running 20 inverse iterations.
Then I try to load the model after 1 epoch, the reconstruction error is suddenly about 5.
I load the model after 50, 150, 200 epochs, but none of them can match the untrained model’s inverse error. After 200 epochs, for a (3x3x32 sized) picture, the smallest error is about 0.95.
When I use inverse iterations on the trained model, the reconstruction error rises when I use more inverse iterations. It’s strange because I think the more inverse iterations I use, the less inverse error I will get.
Is this result normal? This problem puzzles me a lot.