What's the best way to select features independent of the model being used?
There is no best way, but if there was, Tensorflow would definitely not be it. There are three ways I can think of, and each of them is worse than the others in some cases (no free lunch):


Measure correlation of variables with the output variable, and take the variables with the highest correlation. This is a rather poor method as it considers linear correlation, and this is not a very good measure of dependence. Instead of this, although a bit more computationally intensive, is to measure the distance correlation between the output variable and all the variables, and select the features with highest distance correlation.
Fit a linear model with L1 penalization (Lasso). Lasso will automatically select variables for you setting the weights of the non important variables to 0. The variables with the nonzero weights are the ones you can select.
Fit a random forest or a gradient boosting, and take the variables with the highest feature importances. This has worked very well for me in practice.


I hope this helps.