title: Multilayer-perceptron, visualizing decision boundaries (2D) in Python
body: <p>I have programmed a multilayer perception for binary classification. As I understand it, one hidden layer can be represented using just lines as decision boundaries (one line per hidden neuron). This works well and can easily be plotted just using the resulting weights after training. </p>
  
  <p>However, as more layers are added I'm not sure about what approach to use and the visualization part is rarely handled in textbooks. I am wondering, is there a straight forward way of transforming the weight matrices from the different layers to this non-linear decision boundary (assuming 2D inputs)? </p>
  
  <p>Many thanks,</p>