@BeytiKebab

Touch up on the readme and installation

I’m finally on PyPi! It took way longer than I expected to turn the trained model into teh CLI tool but here we are! I also polised and edited the README. It can be installed by python3 -m pip install digitReader and run by classifyDigit –image image.jpeg. Feel free to check out my project demo!

Worked on making the woring project a comman line app that precits the digit in the given picture. Next I will write the readme

Fixed my batch training, my loss calculation was off. Also added weight saving so I dont have to retarin everytime I want to test. And of course seperated test from train! Although it still runs preetry slow 50s train runtime…. I will experinment with different batch sizes, training sizes, number of neurons, learning rates soon. I might also do a version with numpy, my original goal was to not use any libraries to learn the fundementals, but numpy is much faster than python list so I might use that to reduce runtime in a seperate version to see how much of a difference it makes!

I tried to optimize and add more trainign data but accidently made it even less acurate and take longer to process. Will have to do some research!

Trained the network with 10 images (first tried with 100 but it was SO SLOW), but it runs and makes a prediction!!!!! altough it thought 5 was a 3….. I am gonna try figure out ways to optimize my train function next fo I can train 100!

Decoding MINST images! Deconding those with no libraries are harder than I thought and I had to do a TON of research about the data files. But here it is the color value of each pixel of the first imege!! I just have to extract them all, maybe find a better way to store them so I dont have to decode them each time, and pass them to my neural network, which currently have 784 hidden input nurons, 64 hidden neurons and 10 output neurons corresponding to each digit I am trying to predict. Sorry for the super long devlog, I guess I am just excited that its turning into what I am trying to build!

I added 3 output neurons meaning the neural network will respond in tems of class 1: 0.01, class 2: 0.03, class 3: 0.96. To implement that I had to add softmax fucntion chnage my forwardPass and backwardPass implementations to work with new matrixes.

tried to add the go forward logic, i think something is wrong but its getting late…

Added a second varable to the hidden neurons, and organised the code to work with it. Managed to get a very low loss!!!!!

Added silkscreen and resired the PCB

Redid the connections on my PCB for checks to run clear, I migth add some designs next! Note: Previusly I worked on my PCB for 2 hours and 19 minutes but those hourse were accidently logged to my neural network project instead. I asked about the situation in #stardance-help, and support confirmed that the time cannot be reassigned due to Hackatime’s data protections. I mentioned the siyuation in my devlog for my neural network projects devlog too when submmiting the two hours there as adviced. To learn more: https://hackclub.slack.com/archives/C0AP0NMSP3P/p1788725484466219

I worked on the PCB for my Hackpad project for 2 hours and 19 minutes, but accidentally connected the Lapce recording to this project instead. I asked about the situation in #stardance-help, and support confirmed that the time cannot be reassigned due to Hackatime’s data protections. They advised me to mention the situation in both projects’ devlogs so the extra time can be accounted for during review. More details can be found here https://hackclub.slack.com/archives/C0AP0NMSP3P/p1788725484466219

Redid the Schematic, because forgot to add the dsplay. Also included LED’s and keyboard matrix this time!

Got started with the PCB, but realised I FORGOT the OLED screen… Will go back and do that 👍

Did the schematic design!!

Just added cactus obejct not need to generate them continously and add colision detection

Did the backtpolation to work with many x’s and y’s

Added 2 more hidden neurons, set up lists to manage their related variables and set up backpropagation for each.