What's the best way of handling the GAN training output?
Supervising the training of GANs usually involves outputting not only metrics, but also images at a certain interval of epochs. My application also involves printing tables. I use jupyter notebooks, but just printing it all on the notebooks makes each notebook for each experiment way too large (+100 MB), and the internet browser gets slow and crashes often because of that.
I suppose the usual practice would be to save the image outputs somewhere else (either with tensorboard or just plain image files), but that would not be ideal for me because I like to observe each image together with the text/table output relative to it's epoch. It would be even better if I could save the entire training output into a single file, so I could just scroll down through it, observing each epoch output with text/table/image, just like the jupyter notebook output. Is there a way of implementing this? Or perhaps a better way that I'm not considering?
Thank you and sorry if this question is somehow inadequate. If so, let me know and I'll delete it.

Well It's a matter of preference, how someone likes to have it. When I trained a GAN, I handled it in the following way, for the loss and other such values I printed them simply on the notebook per epoch manner as we would do with any other models along with that I would generate an image made by the generator and save it to a folder to check If the generator is getting better. But to make it easier for me to observe those images, I've used meaningful labeling for those images, I've included epoch number in the label, which helps to go through the images pretty quickly. However going through the images would be a bit tedious job, so to make it a bit more comforting I've merged all those pictures produced at every epoch (let's say 500) and made a video, that's pretty cool to watch. I found this way of training my GAN and producing its output really easier but as I said It would be a matter of preference.
Here's the code-
https://github.com/khalidsaifullaah/Classic-Deep-Learning-Models/tree/master/GAN
