10 commits

Author	SHA1	Message	Date
Starbeamrainbowlabs	222a6146ec	write glue for .jsonl.gz → .tfrecord.gz converter	2022-08-08 15:33:59 +01:00
Starbeamrainbowlabs	28a3f578d5	update .gitignore	2022-08-04 16:49:53 +01:00
Starbeamrainbowlabs	323d708692	dataset: add todo ... just why, Tensorflow?! tf.data.TextLineDataset looks almost too good to be true..... and it is, as despite supporting decompressing via gzip(!) it doesn't look like we can convince it to parse JSON :-/	2022-07-26 19:53:18 +01:00
Starbeamrainbowlabs	b53c77a2cb	index.py: call static function name run	2022-07-26 19:51:28 +01:00
Starbeamrainbowlabs	a7ed58fc03	ai: move requirements.txt to the right place	2022-07-26 19:25:11 +01:00
Starbeamrainbowlabs	e93a95f1b3	ai dataset: add if main == main	2022-07-26 19:24:40 +01:00
Starbeamrainbowlabs	de4c3dab17	typo	2022-07-26 19:14:55 +01:00
Starbeamrainbowlabs	18a7d3674b	ai: create (untested) dataset	2022-07-26 19:14:10 +01:00
Starbeamrainbowlabs	dac6919fcd	ai: start creating initial scaffolding	2022-07-25 19:01:10 +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