Neural Network Visualisation in Clojure

Cool, with caveats. Although this is interesting for people who know how neural network structures are built and generally how backpropagation and its successor training algorithms work, it isn't particularly _informative_ as a visualization. It does show how easy it is to encode information visually, compared with how difficult it can be for the viewer to _decode_ that same information. This is a common problem with "information", as opposed to "scientific data" (such as volumetric scan data or vector maps) visualizations, where there's no obvious physical correlative that we can use to help us decode the information as viewers.

I have a question for the author, but please do not interpret this as the typical HackerNews-esque pessimistic attack, as it is a sincere question.<p>Do you really feel like visualizing Neural Networks helps to understand them better? I have yet to find one that has helped me understand it any better than a textual explanation or pseudo-code of the algorithm.

I think it's a useful visualization, but I prefer matrix plots to observe the weights. You can see the weights start differentiating themselves as training proceeds, and you'll notice that some layers tend to learn a lot faster than others. The unit activations (neuron outputs) are similarly useful to visualize.<p>Example of weights on matrix plot: <a href="http://imgur.com/T48Wal1" rel="nofollow">http://imgur.com/T48Wal1</a>

I wrote a commercial NN simulator in the late 1980s and I used a different approach to visualizing weights (that many others also use): if two connected layers are viewed a a 1-dimensional vector, then the connection weights are represented by a 2-dimensional grid. Each weight grid cell is color coded. This is a much more information rich display.

What is the color scale for the connection weight strength?