In an effort to understand a bit more about neural networks, I wrote a Pd external, by translating Python code to C, from the book "Neural Networks from Scratch in Python". After a couple of months of working on this, I ended up with [neuralnet].

This is an object written in pure C, without any dependencies, for creating densely connected neural networks for classification, regression, and binary logistic regression. You can choose among different activation and loss functions, optimizers, and other settable features. I've created some examples, trying to replicate some of the examples in the aforementioned book, and some examples of my own.

The code is on GitHub (https://github.com/alexdrymonitis/neuralnet), and Linux amd64 and armv7-32 (Raspberry Pi) binaries are uploaded to deken. I don't have a Mac or Windows machine, and I don't know how to compile for these architectures on a Linux machine (or if that is even possible). I would be grateful if anyone can compile for any of these architectures and upload to deken.

I would also love to get feedback both on how the object performs, and on the source code itself.

While your finger are still warm, what would be great would be to be able to export "images" of the neural net training, layer by layer, like in all those images blending app.

I think it is a bitmap of the output values of each neuron of a layer.

Maybe you have seen images generated by "IA". They are everywhere know.

They are in fact not the output of a neural network, but are visualisations of the internal process of a layer of a neural network "analysing" or "sorting" an input image.

This ability to see what's going on inside of a neural network was implemented in google's tensor flow for debbuging purpose, if I remember it correctly.

To make it simple, in a neural network with an input vector shaped as a screen, with each input being a pixel, and the pixels organised in lines one above the other, a code to generate images from, let say, instant output value of each pixels of a layer, or other values coresponding to the differents internal parameters of each neuron of a same layer.

I think a .bitmap format is straitforward to generate.

Hi, I am trying out this external, curious about it.

Would it be possible for you to describe a bit more on how to explore and work with the examples provided? I have only a very basic understanding of neural networks and have not read the book you base the examples on.