I added a clear button so you can clear the board and try out you own creations.

Life Like Cellular Automata

Cellular Automata is a discrete grid based system which consists of cells that update themselves following a certain ruleset over discrete time chunks called generations. Conway’s game of life is the most famous one of these, but is only 1 subset of a larger set of life-like cellular automata.

In Conways game of life, each cell can either be one of 2 states, dead or alive. Its’s updating ruleset is:

• A node is ‘born’ if it has 3 neighbours, changing state from dead to alive
• A node survives if it is alive and has either 2 or 3 neighbours, else it reverts back to a dead state

This ruleset can be written in the form of B3/S23 and is just one of many other rules that can exist in life-like Cellular Automata.

In this update, I have added boxes that you can turn on or off to customise the cellular automa to any rule you want.
My next goals are to:

• Make the grid editable so you can create your own systems
• Make a rewind button so you can see how different structures are formed
• Create a slider to control simulation speed

Mainly, I just added checkboxes. The vision is to have these boxes control at what numbers nodes spawn and die.

Now that I had a drawable grid, i converted that grid to a pytorch tensor, that I plugged into my MNIST classifer that I made 2 devlogs ago, to create an AI number guesser! Honestly, while it does ocassionally slip up and make a few mistakes, especially confusing 7s and 2s and 6s and 0s, it is amazing!

I created a pygame grid that you can draw on, left click to add, right click to remove.

Using my knoeledge on classification I created a convolutional neural network classifier on the Mnist numbers dataset:

Added a Mouse scroll to make the zoom mechanic more intuitave - and its much less laggy than a slider. Programming a scroll wheel was both easier and harder than I expected, but I have now added a new tool to my pygame toolkit.

I Implemented the slider mechanic I was working on before in order to make it so that you can scale the simulation, as can be seen in this video.

I followed learn pytorch to make my first Convolutional Neural Network in pytorch! I have it sucessfully trained on the black and white mnist fashion dataset so it can identify fashion objects. Now I think I have enough foundations to build my final classification project.

I tried to make a classifier on skit-learns moon dataset that creates 2 classes of blobs in a cresent shape. Using my knowledge on how to create a binary classifer with ReLu and Binary cross entropy loss, I created a classifier that sucessful ‘solved’ the toy dataset. After completing this, I began to work on learnpytorch’s Computer vision module. My vision is to use my knowledge of computer vision and classification to build an interactive CNN visualiser.

I removed my old colour stuff and implemented conways game of line my changing the update code. I also made it screen size.

I made my first working classifier in pytorch with 0 training loss!

I am following learnpytorch.io’s code to create a simple binary classifier. It seems that I have a long way to go.

Finally finished my project. I expected this project to only take like 5 hours max, but it took more than double that! I have learnt a lot about pygame and pytorch in this project. My final additons were a databox that showed ML metrics like epochs, and added a formula to the line on the graph.

Made my program much smoother and added a stop button.

Finally, After heaps of normalisation and code and bugfixing, I finally, have a working linear regressor!!!

Created a graph wher eyou can click nodes, delete them and clear the screen

Created a linear regressor

Created a slider