Hello!

I am a computer science student researcher who is doing a PhD at the University of Hull. My project title is Using Big Data and AI to dynamically predict flood risk.

I started out teaching myself about various web technologies, and then I managed to get a place at University, where I am now. I've previously done a degree (BSc Computer Science) and a Masters (MSc Computer Science with Security and Distributed Computing) at the University of Hull. I've done a year in industry too, which I found to be particuarly helpful in learning about the workplace and the world.

I currently know C# + Monogame / XNA (+ WPF), HTML5, CSS3, Javascript (ES7 + Node.js), PHP, C / C++ (mainly for Arduino), some Rust, and Python. Oh yeah, and I can use XSLT too.

I love to experiment and learn about new things on a regular basis. You can find some of the things that I've done in the labs and code sections of this website, or on GitHub (both in my personal time and for my PhD). My current big personal projects are Pepperminty Wiki, an entire wiki engine in a single file (the source code is spread across multiple files - don't worry!), and WorldEditAdditions. Nibriboard (a multi-user real-time infinite whiteboard), although the latter is in its very early stages. -->

I can also be found in a number of other different places around the web. I've compiled a list of the places that I can remember below.

Social

Other

I can be contacted at the email address webmaster at starbeamrainbowlabs dot com. Suggestions, bug reports and constructive criticism are always welcome.

For those looking for my GPG key, you can find it here. My key id is C2F7843F9ADF9FEE264ACB9CC1C6C0BB001E1725, and is uploaded to the public keyserver network, so you can download it with GPG like so: gpg --keyserver hkps://keyserver.ubuntu.com:443/ --recv-keys C2F7843F9ADF9FEE264ACB9CC1C6C0BB001E1725

## Blog

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### Latest Post

#### 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

By on

## Tools

I find useful tools on the internet occasionally. I will list them here.

Art by Mythdael