title: Neural Network composed of multiple activation functions
body: <p>I am using the sknn package to build a neural network. In order to optimize the parameters of the neural net for the dataset I am using I am using an evolutionary algorithm. Since the package allows me to build a neural net where each layer has a different activation function, I was wondering if that is a practical choice, or whether I should just use one activation function per net? Does having multiple activation functions in a neural net harm, does no damage, or benefit the neural network?</p>
  
  <p>Also what is the maximum amount of neuron per layer I should have, and the maximum amount of layers per net should I have?</p>