On device training of TFLite

Can I add more personalization layers to on device training of TFlite, it seems that only the head layer can be added in the case?

I’m very sorry, it was my mistake, I’ve figured out how to add more layers as well as train it flexibly (defining the relevant interpreter functions on the server), thank you!

I am asking myself if it’s possible to change the last layer (or any layer) of my TFLite model on the device. I know that updating weights is possible. But let’s say, I have a classification problem with a Dense layer in the end giving me 10 outputs, and I want to add a new class. This means I want to extend my Dense layer from 10 to 11. Is that possible? And if so, how? Thanks in advance!

Hi @ konak

You have mentioned On device weight updation of tflite model is possible. Can you please share some resources regarding On-device weight updation in IOT devices?