This repository contains a simple demo showcasing training of a simple MLP on the MNIST dataset.

This demo is part of a talk given at the BCGS weekend seminar in Bad Honnef on <2018-05-26 Sa>.

The basic idea is the following:

• train MLP using Arraymancer
• after each epoch (or more or less often?) take (some?) random digit, classifiy
• use plotly (Nim interface to plotly.js) to plot sample as heatmap on the fly, including prediction of network of that sample

• ann_talk.org is the talk I will be giving
  • ann_talk.pdf the corresponding PDF. It’s created via org-beamer export
• src: contains the Nim code
  • src/clientPlotly.nim: The Nim program, which compiles to javascript and performs live plotting of the data it’s being sent
  • src/index_style.html The Html template, which runs the compiled clientPlotly.js
  • src/server.nim The Nim program, which compiles to C and trains the MLP and sends the data to clientPlotly.js
  • src/protocol.nim The (very simple) protocol used to transfer the data between the two programs using WebSocket
  • src/resources: A folder containing a file, which contains data used to initialize the plots.

The demo consists of 2 different programs, a client and a server. The server performs the actual training using Arraymancer. The client is only used to plot the data. It uses nim-plotly (based on plotly.js) for that.

Since nim-plotly by default uses the C backend, some work had to be done to be able to produce live updating plots. Using a static template.html does not easily allow to call Plotly.react to replace the content of the plot. Therefore, we use an experimental JS backend for nim-plotly and call newPlot as well as react from that. But because we cannot train the MLP using the JS backend, we need to split the two into individual programs. The communication between the two happens using WebSockets, with the data being sent as stringified JSON.

Note that this depends on the nim-plotly/exp_js_backend branch here: https://github.com/vindaar/nim-plotly/tree/exp_js_backend.