I'm trying to make a simple gesture recognition system to use with my Raspberry Pi equipped with a camera. I would like to train a neural network with tensorflow on my more powerful laptop and then transfer it to the RPi for prediction (as part of a Magic Mirror). Is there a way to export the trained network and weights and use a lightweight version of tensorflow for the linear algebra and prediction without the overhead of all the symbolic graph machinery that are necessary for training? I have seen the tutorials on tensorflow server, but I'd rather not set up a server and just have it run the prediction on the RPi.
Yes, possible and available in the source repository. This allows to deploy and run a model trained on your laptop. Note that this is the same model, which can be big.
To deal with size and efficiency, TF is currently moving along a quantization approach. After your model is trained, a few extra steps allow to "translate" it into a lighter model with similar accuracy. Currently, the implementation is quite slow, though. There is a recent post that shows the whole process for iOS---pretty similar to RaspberryPI overall.
The Makefile contribution is also quite relevant for tuning and extra configuration.
Beware that this code moves often and breaks. It is sometimes useful to checkout an old "release" tag to get something that works end to end.