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Author	SHA1	Message	Date
Starbeamrainbowlabs	1297f41105	.tfrecord files are too much hassle ... let's go with a standard of .jsonl.gz instead	2022-07-01 18:28:39 +01:00
Starbeamrainbowlabs	f5f267c6b6	Update dependencies	2022-07-01 16:56:51 +01:00
Starbeamrainbowlabs	ba258fbba0	Remove debug loogging	2022-05-19 19:25:44 +01:00
Starbeamrainbowlabs	e030e6c2d5	Fix remaining(?) crashes= in our code	2022-05-19 19:13:28 +01:00
Starbeamrainbowlabs	3cb7e42505	it doesn't crash as much now, but it still isn't behaving.	2022-05-19 18:52:15 +01:00
Starbeamrainbowlabs	bb018c53f6	Fix many bugs ... Many bugs remain though	2022-05-19 17:54:14 +01:00
Starbeamrainbowlabs	cc5efbae8a	Implement tfrecodify subcommand. ... It's all still untested, but that's the next step	2022-05-19 17:15:15 +01:00
Starbeamrainbowlabs	0fa7ae9d6a	Imnplement plumbing, but it's all untested	2022-05-18 17:47:02 +01:00
Starbeamrainbowlabs	bf4866bdbc	Add data readers	2022-05-18 17:04:11 +01:00
Starbeamrainbowlabs	5829db062b	Merge branch 'main' of git.starbeamrainbowlabs.com:sbrl/PhD-Rainfall-Radar	2022-05-13 19:09:03 +01:00
Starbeamrainbowlabs	58dfaf2ece	Initial commit	2022-05-13 19:08:14 +01:00
Starbeamrainbowlabs	9411ad3218	tweak licence	2022-05-13 19:08:04 +01:00
Starbeamrainbowlabs	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