CPPN mini-project

Neural networks and CPPNs are slightly different. First, CPPNs are used as generators of content, whereas neural networks are used for making predictions from data. Furthermore, there is a point to using various activation functions in the nodes of a CPPN, because the activation functions affect the geometry of the patterns generated. Image from the book Procedural Content Generation in Games — A textbook and an overview of current research (Noor Shaker, Julian Togelius, and Mark J. Nelson).
Image from https://kwj2104.github.io/2018/cppngan/
The CPPN can be regarded as a function that takes an (x,y) image coordinate, distance r between (x,y) and (0,0), and a latent vector k as input and returns a grayscale value. The function is called for each (x, y) coordinate in the intended output image. Image from https://kwj2104.github.io/2018/cppngan/
Art made by my very first CPPN network.
Image from https://www.aaai.org/ocs/index.php/AIIDE/AIIDE12/paper/view/5449
Screenshot of my flower generator from http://pcg-mini-project.herokuapp.com/cppnflowers2. From left to right: flower 1, flower 2, and flower 3, which results from breeding flower 1 and 2.
Screenshot of my tshirt-flowa-powa-generator from http://pcg-mini-project.herokuapp.com/cppnflowers3

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