• Aatube@kbin.social
    link
    fedilink
    arrow-up
    0
    ·
    9 months ago
    1. Specifying weights, biases and shape definitely makes a graph.
    2. IMO having a lot of more preferred and more deprecated routes is quite close to a flowchart except there’s a lot more routes. The principles of how these work is quite similar.
    • General_Effort@lemmy.world
      link
      fedilink
      English
      arrow-up
      0
      ·
      9 months ago
      1. There are graph neural networks (meaning NNs that work on graphs), but I don’t think that’s what is used here.

      2. I do not understand what you mean by “routes”. I suspect that you have misunderstood something fundamental.

      • Aatube@kbin.social
        link
        fedilink
        arrow-up
        0
        ·
        9 months ago
        1. I’m not talking about that. What’s weights, biases and shape if not a graph?
        2. By routes, I mean that the path of the graph doesn’t necessarily converge and that it is often more tree-like.
        • General_Effort@lemmy.world
          link
          fedilink
          English
          arrow-up
          0
          ·
          edit-2
          9 months ago

          You can see a neural net as a graph in that the neurons are connected nodes. I don’t believe that graph theory is very helpful, though. The weights are parameters in a system of linear equations; the numbers in a matrix/tensor. That’s not how the term is used in graph theory, AFAIK.

          ETA: What you say about “routes” (=paths?) is something that I can only make sense of, if I assume that you misunderstood something. Else, I simply don’t know what that is talking about.

          • Natanael@slrpnk.net
            link
            fedilink
            English
            arrow-up
            0
            ·
            9 months ago

            If you look at the nodes which are most likely to trigger from given inputs then you can draw paths