I've been using TensorFlow to create feed-forward neural nets with customized initial weights and biases (cannot provide code for proprietary reasons, but another algorithm outputs initial matrices that are then plugged into tf for optimization). Everything has been working beautifully for classification, however when I started testing on regression datasets I was getting enormously large errors. I ran the skflow Boston housing data example (DNN regressor) and it gets much better results. Like others (tensorflow model has different results than the same model in skflow (optimizer)) I tried reproducing the skflow code in tf (abandoning my weight initialization idea) and am getting drastically worse performance in tf.
So I'd like ideas on one of two things, either: 1. How can you initialize with custom weight matrices in skflow? Digging in the source code has yet to reveal simple solutions... or 2. What is the key difference between the skflow dnn regressor and the code written in tensorflow (what is the answer to the question I linked to)?