Hello, folks.

I built a simple classification neural net in `Flux`

(6 inputs in a `Dense`

layer, a `BatchNorm`

hidden layer with 13 neurons, another `Dense`

13 neuron output layer) and I was looking for some way to correlate the outputs and inputs. For instance, based on the weights assigned by `Flux`

, what are the input neurons that are most important?

For instance: let’s say one of the outputs is “cat” and one is “mouse” and the inputs are height, number of legs, etc. By looking at the weights, maybe I can figure out that the most important distinguishing factor between a “cat” and a “mouse” is the height. So I’m looking for an elegant way to plot that.

I noticed that for the `Dense`

layers I can use `layer.weight`

, but I’m not sure for the `BatchNorm`

layer. Also, the weight matrices are large, so I’m looking for a clever way to look at all the weights easily for a given input. Maybe a graph where the lines connecting the neurons are colored by the weights?

Thanks a lot!