Nicholas Carlini

Sorry I don't understand quite what you want. Could you expand?

`y_in` here gets set with the weakly and strongly augmented images. https://github.com/google-research/fixmatch/blob/d4985a158065947dba803e626ee9a6721709c570/fixmatch.py#L89 What do you want to do with the augmentations?

The labeled images will get passed as part of the unlabeled set as well, and that's how the labeled images are strongly augmented. I'm still confused what your overall objective...

FixMatch does not strongly augment each of the labeled images in the batch on every iteration. It only weakly augments those images. The only time the labeled images become strongly...

`xt_in` has the training examples, and `x_in` has the evaluation images. You can see that `x_in` is only used in the classification ops: https://github.com/google-research/fixmatch/blob/d4985a158065947dba803e626ee9a6721709c570/fixmatch.py#L137-L138

Yeah, the code is a bit convoluted. It evolved from three projects. If you'd like to find a simpler implementation David wrote one here https://github.com/google/objax/tree/master/examples/fixmatch However, for your question, no...

Sorry for the late reply. If you haven't figured it out already, probe here is used to check how accurate the model is on these `probe` images as a way...

Is the GPU active when you're running the training? Or is that stalling too?

I just wanted to follow up to see if you worked anything out. I don't know if I have any ideas for what could be causing this with our code,...

Huh. Two ideas maybe: 1. Does CPU-only training work? It would be really slow, but it should at least not stall. 2. If you try to train a MixMatch model...