How do we know which dimension of data impacts most on the predicted value?

I am training a model with neural network (linear+relu). Given each data `x` represented as multi-dimension

x=(x1, x2, …, xm)

I’m training a model to predict a value `y`. How do I know which element among `(x1, x2, …, xm)` affects `y` most? Some of elements are independent/uncorrelated, so I can remove, but I don’t know which one is.

I know we do dimensional reduction but with that method can we know which element matters and which element is unnecessary? …

data deeplearning element impacts linear network neural network relu training value