Quelle: https://freddyayala.github.io/Prismata/ Werkzeuge: Python (scikit-learn, Transformers), Three.js (WebGL). Daten: Aus Hugging Face-Modellen extrahierte Gewichte.
Erläuterung: Dieses interaktive Tool projiziert die hochdimensionalen Gewichtsmatrizen neuronaler Netze mithilfe von PCA in den 3D-Raum. Es ermöglicht uns, die architektonische Entwicklung von einfachen CNNs (LeNet) zu komplexen Transformern (GPT-2) zu verfolgen.
Von frayala87
2 Kommentare
[deleted]
I like this concept I think.
The viewer runs a bit hot — still is using a lot of CPU to just rest on its regular rotation after I’ve finished manipulating it.
The colored blocks are too large and, they become an indistinguished blob, and zooming in does not give any indication on perhaps what we should be looking at with the plotting axes.
I suppose I can look at the code for this, but how are you selecting which nodes and weights to plot? Because I assume there’s either a criterion for rendering or else you’ve also searched for representative clusters of nodes to simplify the plotting (which might be doable in the same run as PCA) — in which case I’d expect a color key that indicates roughly the size and strength of each block.
These are just some thoughts, not knowing the code.
I’m not sure if you can make the layering a bit more obvious too — that kinda goes along with the general blob effect that those big blocks for nodes are giving.
But it seems a great starting point for doing this kind of visualization. A cool step after tweaking it would be to show how information moves and is modified in each layer, animated during an actual run.