As part of a project I'm working on, I have built a "fully connected layer" (multilayered perceptron) network for image classification. Even though I know how to build an convolutional NN, for various reasons I had to use a fully connected one.
My question is: given the network, learned to classify with test accuracy of 86%, how can I visualize what each hidden neuron is looking for? And, more broadly, how can I understand better how the network decides how to classify? I have seen many ways to discover this in CNN, but not in fully connected networks.
I used tensorflow to build the network.