Commit graph

458 commits

Author SHA1 Message Date
bb018c53f6
Fix many bugs
Many bugs remain though
2022-05-19 17:54:14 +01:00
cc5efbae8a
Implement tfrecodify subcommand.
It's all still untested, but that's the next step
2022-05-19 17:15:15 +01:00
0fa7ae9d6a
Imnplement plumbing, but it's all untested 2022-05-18 17:47:02 +01:00
bf4866bdbc
Add data readers 2022-05-18 17:04:11 +01:00
5829db062b
Merge branch 'main' of git.starbeamrainbowlabs.com:sbrl/PhD-Rainfall-Radar 2022-05-13 19:09:03 +01:00
58dfaf2ece Initial commit 2022-05-13 19:08:14 +01:00
9411ad3218
tweak licence 2022-05-13 19:08:04 +01:00
8a9cd6c1c0
Lay out some basic scaffolding
I *really* hope this works. This is the 3rd major revision of this
model. I've learnt a ton of stuff between now and my last attempt, so
here's hoping that all goes well :D

The basic idea behind this attempt is *Contrastive Learning*. If we
don't get anything useful with this approach, then we can assume that
it's not really possible / feasible.

Something we need to watch out for is the variance (or rather lack
thereof) in the dataset. We have 1.5M timesteps, but not a whole lot
will be happening in most of those....

We may need to analyse the variance of the water depth data and extract
a subsample that's more balanced.
2022-05-13 19:06:15 +01:00