ORIGINAL IDEA from https://github.com/MaxRobinsonTheGreat/mandelbrotnn. I reproduced his experiments with limited ressources. Basically he uses fourier features to approximate the mandelbrot set with a big neural network (~8M parameters). I did it with 600k parameters with a "pseudo-Fourier 2D features", and I'm pretty satisfied with the result.

Training GIF :

Final Approximation (supposedly 8K resolution ?) :

My result is not as good as MaxRobinson's.

• python3
• pip3

Install dependencies with:

pip install -r requirements.txt

Then run python main.py for some example uses. Take a look at the main.py file for some example functions on how to use the modules.