Determining which entries in input vector have the greatest effect on output vector I'm looking for a solution in Python. I'm doing a project where I've built a densely packed neural network which takes a 48-dimensional input vector and the last layer is a 2-dimensional linear output. I'm looking for an algorithm that can determine how much each scalar in the input vector contributes to the output vector.

Specifically, my input vector is 24 consecutive pairs of position and momentum. For example the input would be $(x_1,v_1,x_2,v_2,...,x_{24},v_{24})$ and my output is the next position and momentum pair $(x_{25},v_{25})$. I've hypothesized that the more recent (higher index) pair should impact the output more. Is there any technique that I could use to quantitatively verify this?
I'm not very experienced in the interpretation of neural networks but I have two suggestions. 
1: Alter the values of the input, one feature at a time, and watch how the output varies.
2: Use another model with more interpretability. You could do this just for interpretation.