𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿 𝗧𝗮𝗸𝗲 𝗢𝗻: 𝗛𝗶𝗴𝗵-𝗥𝗲𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝗡𝗲𝘂𝗿𝗮𝗹 𝗖𝗲𝗹𝗹𝘂𝗹𝗮𝗿 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗮
Art and math meet in machine learning.
Cellular automata use a grid of cells. Each cell changes state based on fixed rules. Conway's Game of Life is a famous example. You follow simple rules to create complex patterns.
Neural Cellular Automata (NCA) changes this. Instead of fixed rules, a neural network learns the rules from data. The network predicts the next state of a cell by looking at its neighbors. This produces complex and surreal visuals.
Generating high-resolution images is hard. As the grid grows, the math becomes too complex for the network.
I use a technique called pixel shuffle to fix this.
- I downsample the grid to a lower resolution.
- I train the network on this smaller grid.
- I upsample the output to get a high-resolution image.
You can implement this using PyTorch. Here is a simple way to structure an NCA model:
import torch import torch.nn as nn import torch.optim as optim
class NCA(nn.Module): def init(self): super(NCA, self).init() self.conv = nn.Conv2d(3, 64, kernel_size=3, padding=1)
def forward(self, x):
x = torch.relu(self.conv(x))
x = torch.max_pool2d(x, 2)
x = torch.relu(self.conv(x))
x = torch.max_pool2d(x, 2)
return x
NCA allows you to bridge the gap between code and digital art. It is an active field for scientific visualization and creative design.
Optional learning community: https://t.me/GyaanSetuAi