Commit graph

67 commits

Author SHA1 Message Date
b9a018f9a9
properly close all teh streams 2022-07-08 16:51:17 +01:00
1a657bd653
add new uniq subcommand
It deduplicates lines in the files, with the potential to add the ability to filter on a specific property later.
The reasoningf or this is thus:
1. There will naturally be periods of time where nothing happens
2. Too many duplicates will interfere and confuse with the contrastive learning algorithm, as in each batch it will have less variance in samples

This is especially important because contrastive learning causes it to compare every item in each batch with every othear item in the batch.
2022-07-04 19:46:06 +01:00
234e2b7978
Write \n end of line character
we actually forgot this, wow....
2022-07-04 17:05:05 +01:00
920cc3feaf
Properly close last writer
otherwise Node.js doesn't quit
2022-07-04 17:04:11 +01:00
588ee87b83
Bugfix: fix end-of-file 2022-07-01 19:34:26 +01:00
5b2d71f41f
it works
.....I think
2022-07-01 19:08:36 +01:00
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
f5f267c6b6
Update dependencies 2022-07-01 16:56:51 +01:00
ba258fbba0
Remove debug loogging 2022-05-19 19:25:44 +01:00
e030e6c2d5
Fix remaining(?) crashes= in our code 2022-05-19 19:13:28 +01:00
3cb7e42505
it doesn't crash as much now, but it still isn't behaving. 2022-05-19 18:52:15 +01:00
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
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