## Visualising Tensorflow model summaries

It's no secret that my PhD is based in machine learning / AI (my specific title is "Using Big Data and AI to dynamically map flood risk"). Recently a problem I have been plagued with is quickly understanding the architecture of new (and old) models I've come across at a high level. I could read the paper a model comes from in detail (and I do this regularly), but it's much less complicated and much easier to understand if I can visualise it in a flowchart.

To remedy this, I've written a neat little tool that does just this. When you're training a new AI model, one of the things that it's common to print out is the summary of the model (I make it a habit to always print this out for later inspection, comparison, and debugging), like this:

model = setup_model()
model.summary()

This might print something like this:

Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #
=================================================================
embedding (Embedding)        (None, 500, 16)           160000

lstm (LSTM)                  (None, 32)                6272

dense (Dense)                (None, 1)                 33
=================================================================
Total params: 166,305
Trainable params: 166,305
Non-trainable params: 0
_________________________________________________________________

(Source: ChatGPT)

This is just a simple model, but it is common for larger ones to have hundreds of layers, like this one I'm currently playing with as part of my research:

(Can't see the above? Try a direct link.)

Woah, that's some model! It must be really complicated. How are we supposed to make sense of it?

If you look closely, you'll notice that it has a Connected to column, as it's not a linear tf.keras.Sequential() model. We can use that to plot a flowchart!

This is what the tool I've written generates, using a graphing library called nomnoml. It parses the Tensorflow summary, and then compiles it into a flowchart. Here's a sample:

It's a purely web-based tool - no data leaves your client. Try it out for yourself here:

https://starbeamrainbowlabs.com/labs/tfsummaryvis/

For the curious, the source code for this tool is available here:

https://git.starbeamrainbowlabs.com/sbrl/tfsummaryvis

## Tag Cloud

3d 3d printing account algorithms android announcement architecture archives arduino artificial intelligence artix assembly async audio automation backups bash batch blender blog bookmarklet booting bug hunting c sharp c++ challenge chrome os cluster code codepen coding conundrums coding conundrums evolved command line compilers compiling compression containerisation css dailyprogrammer data analysis debugging demystification distributed computing dns docker documentation downtime electronics email embedded systems encryption es6 features ethics event experiment external first impressions freeside future game github github gist gitlab graphics hardware hardware meetup holiday holidays html html5 html5 canvas infrastructure interfaces internet interoperability io.js jabber jam javascript js bin labs learning library linux lora low level lua maintenance manjaro minetest network networking nibriboard node.js open source operating systems optimisation own your code pepperminty wiki performance phd photos php pixelbot portable privacy problem solving programming problems project projects prolog protocol protocols pseudo 3d python reddit redis reference releases rendering resource review rust searching secrets security series list server software sorting source code control statistics storage svg systemquery talks technical terminal textures thoughts three thing game three.js tool tutorial tutorials twitter ubuntu university update updates upgrade version control virtual reality virtualisation visual web website windows windows 10 worldeditadditions xmpp xslt

Art by Mythdael