Playing around with generative art through compositional pattern-producing networks, neural networks that map (x, y) -> (r, g, b).
Initially written in January 2021 to make desktop backgrounds. Includes code and demos of symmetric CPPNs, using bitmaps as inputs, and animations, including looping.
cppn_random.ipynb
: Random fully-connected networks, including enforcing symmetries.cppn_conv.ipynb
: Random convolutional networks.cppn_paramcircle.ipynb
: Using a controllable input channel to add structured patterns to the CPPN output.cppn_image.ipynb
: Using an image bitmap as the input coordinate, effectively using the CPPN as a nonlinear colormap for an existing raster.cppn_time.ipynb
: Making animations by running a CPPN with a time dimension. Animations can be looped by walking parameters around a high-dimensional circle.cppn_styletransfer.ipynb
: Optimizing a CPPN to match the style of a target image. This one doesn't really work.