This project demonstrates how to train a hybrid network with caffe and tensorflow components end-to-end. The code implements a trivial 3-layer model so that net outputs and updated weights can be verified by hand-calculated values (and are verified to be correct). The code is structured such that more complicated components can be easily plugged in.
Read the comments within caffe_tf.py for details. The other program
tf_caffe.py implements the archetecture the other way around.