Ok you guys, we have working neural network optimization! For the moment there is only the option to optimize using a neuroevulution algorithm, but more will come in the future.

You can select the input features, target one, change some settings, lay back and get back an optimized version of your neural network.

This happens using pytorch models, i’m kind of moving away from .onnx, as they represent an “imovable” final version of a model to say so, and using the descriptor model me and my friend made will allow us to make visually editing models easier.

Now, optimizing spits out both a .pt2 and a .onnx file, as the onnx file is directly usable.

These changes required some modifications to the .mylo project file structure, but it’s not hard to add new stuff to it.

Next, I will have to polish the UI, add some more animations and also handle the posibility to upload both a .pt2 for working and a .onnx for visualisation only in the same project.

Besides that I also want to make like a “git” where people will be able to publish their models and stuff for you to use.

I have plenty planned, i really like how this project is coming along.