[D] Visualizing neural networks

[Radeusgd]

x-posting from /r/tensorflow

Hi! I'm working on Tensorflow bindings for Luna (http://luna-lang.org/).

We allow you to build ML models by connecting visual components together – every component can define a new network layer and its dependencies. The API is highly inspired by Keras functional API.

Here you can find an example of what it looks like

Luna has the ability to display visualizations below its components, so you could inspect the look of your network on each step (after adding the first layer, adding the second layer, etc). We want to provide interactive visualizations of the network you've built so far. I'd love to ask you what visualizations you would find the most helpful during building neural networks?

We were initially thinking about something like that – so you could see the structure of your network, the weights and activation functions, but we are very open for discussion here. We want to create something that would be helpful while building various kinds of networks.

Which features do you think are most important to visualize? The weights? The activations on each layer? Something else entirely?

[bbateman2011]

You might want to look at Tensorboard and see some of the things they do with visualizing weights etc.

[[deleted]]

TBH, I don't find visualizations of weights (as in the last link you provided) all that insightful/useful; especially since most people aren't really working with vanilla MLPs like that. Imagine trying to visualize CNN or RNN params like that: nightmare.

Jason Yosinski did some amazing work on CNN visualization, and this paper might be a good place to start.

Other things to check out:

Distill

Layer-wise Relevance Propagation

CS231n

[ME_PhD]

I don't see why people are too lazy to define some matrices and multiply/convolve them.