Given that:



 WINTERSDORFF_Raphael:

Could there be a problem with the tensorflow version that I’m using? It’s not likely the problem, but I think I have an earlier version than the one used on colab.


you may try running the tutorial with v2.5 on your local machine and check if that fixes your issue.
The notebook example is runnable in Colab end-to-end and Colab is loaded with the current latest version (TF 2.5).




 WINTERSDORFF_Raphael:

Either nothing happens or the kernel dies. Is there a way to fix or circumvent that issue?


This might be due to the compute/etc requirements of the example, but we don’t have all information about your setup to be able to completely debug this. As you’re probably aware, it’s not a small dataset for a typical demo:

“… large download ahead . You’ll use the training set, which is a 13GB file…”

And the Caching the features extracted from InceptionV3 step can be compute intensive. It comes with a warning in the tutorial:

“You will pre-process each image with InceptionV3 and cache the output to disk. Caching the output in RAM would be faster but also memory intensive, requiring 8 * 8 * 2048 floats per image. At the time of writing, this exceeds the memory limitations of Colab (currently 12GB of memory).”

Also keeping in mind that, as the doc says:

“Performance could be improved with a more sophisticated caching strategy (for example, by sharding the images to reduce random access disk I/O), but that would require more code.”

Maybe some or all of that are contributing to the issue.
Let us know if upgrading to TF 2.5 fixes anything.