Visualizing deep neural network training

Question

I'm trying to find an equivalent of Hinton Diagrams for multilayer networks to plot the weights during training.

The trained network is somewhat similar to a Deep SRN, i.e. it has a high number of multiple weight matrices which would make the simultaneous plot of several Hinton Diagrams visually confusing.

Does anyone know of a good way to visualize the weight update process for recurrent networks with multiple layers?

I haven't found much papers on the topic. I was thinking to display time-related information on the weights per layer instead if I can't come up with something. E.g. the weight-delta over time for each layer (omitting the use of every single connection). PCA is another possibility, though I'd like to not produce much additional computations, since the visualization is done online during training.

Answer 1

The closes thing I know is ConvNetJS:

ConvNetJS is a Javascript library for training Deep Learning models (mainly Neural Networks) entirely in your browser. Open a tab and you're training. No software requirements, no compilers, no installations, no GPUs, no sweat.

Demos on this site plot weighs and how do they change with time (bear in mind, its many parameters, as practical networks do have a lot of neurons). Moreover, if you are not satisfied with their plotting, there is access to networks parameters and you can plot as you wish (since it is JavaScript).

Answer 2

Based on my cursory understanding of the topics, associated with your question, I think that Gephi (https://gephi.github.io; the original gephi.org link redirects there) should be able to handle neural network dynamic visualization. It seems that, in order to achieve your goal, you need to stream your graph(s) with corresponding weights (https://forum.gephi.org/viewtopic.php?t=1875). For streaming, you most likely will need this plug-in: https://marketplace.gephi.org/plugin/graph-streaming.

UPDATE: You may also find useful SoNIA software: http://web.stanford.edu/group/sonia.