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Show HN: TensorSpace.js – Neural network 3D visualization framework

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Re: Show HN: TensorSpace.js – Neural network 3D visualization framework

#6
This is neat! I hope you don't mind a bit of constructive criticism here, but early on in my research career I also thought it would be a good idea to "visualize the neural network connectome" in 3D (I implemented a very rudimentary version of your Browser-based visualizer in QT + OpenGL, no training frontend).

And then I followed up with an early TensorFlow visualizer https://github.com/ericjang/tdb

It turns out that while such tools seem useful at first glance, they turn out to not be that helpful to power users. For models bigger than LeNet, things get really ugly to visualize. And once you understand a high-level module and can take its training for granted, there isn't a need anymore to really look at it anymore. It can also be kind of annoying to tumble around in 3D when you just want to look at some activation maps. What does the 3D aspect of the visualization buy you here?

Tools like TensorBoard + Jupyter notebooks for inspecting weights and ad-hoc visualizations (e.g. VizDom) seem to strike the right balance.

If you want to continue pushing in this direction, I highly encourage embarking on an actual Deep Learning research project using your tool. In ML it's so important to dogfood your own software!

Re: Show HN: TensorSpace.js – Neural network 3D visualization framework

#9
post #6

This is neat! I hope you don't mind a bit of constructive criticism here, but early on in my research career I also thought it would be a good idea to "visualize the neural network connectome" in 3D (I implemented a very rudimentary version of your Browser-based visualizer in QT + OpenGL, no training frontend). And then I followed up with an early TensorFlow visualizer https://github.com/ericjang/tdb It turns out tha…

Are there any good guides, tutorials, or research papers that investigate or advise how to inspect weights during training for debugging. The only things I read are to watch out for vanishing gradients, and when fine-tuning the most change in layers are seen toward the end of the network, not the beginning layers.
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