Estimating importance of variables in a multilayer perceptron From online search so far, I have only found Garson Algorithm as a method for deducing the importance of variables in a Multilayer Perceptron. However the current Garson algorithm included in the 'NeuralNetTools' package for R calculates the importance for a MLP with single hidden layer only and doesn't work for 2 hidden layers.
Is there any other package that runs Garson algorithm on MLP with 2 hidden layers?
Also, is there any other approach to calculate the importance of variables in a MLP apart from Garson algorithm?
 A: Why not trying the same approach as the one used for random forests ? Given a train/test set split of the data, you train your model then test it and observe the error.
Now, for each column of the test set, generate a random permutation of the elements and observe the new error. If the change observed is not important, then the predictor had little impact on the forecast.
For a more accurate estimation of the importance, you can perform a K-fold CV instead of just splitting the data.
I never saw anyone using it with neural networks. However, it applies successfully to SVMs.
The good thing with this approach is that it is completely independent of the learning method you are using - you can implement it once and for all.
A: Have you considered the Eric-Wan diagrammatic jacobian with the Delta rule? 
There are two parts to this. 


*

*The first is the (revolutionary) diagrammatic Jacobian. Before Wan
the gradients were each analytically derived, and a single learning
approach could be a thesis. He gave a general solution for the field.
(here is the link)

*The second part is the delta method. It relates to Jacobian to the
variance. (math.montana.edu/~parker/PattersonStats/Delta.pdf).


BTW: 
Here is a ref that suggests "Garson" is pretty bad. (http://www.massey.ac.nz/~mkjoy/pdf/Olden,Joy&DeathEM.pdf) 
Given that "sensitivity analysis" performs fairly well, you might like this link:
https://beckmw.wordpress.com/2013/10/07/sensitivity-analysis-for-neural-networks/
