efe41b96ec
fixup
2022-09-02 17:58:02 +01:00
f8ee0afca1
ai: fix arch_name plumbing
2022-09-02 17:57:07 +01:00
3d44831080
Add NO_PREFETCH env var
2022-09-02 17:55:04 +01:00
3e0ca6a315
ai: fix summary file writing; make water encoder smaller
2022-09-02 17:51:45 +01:00
389216b391
reordering
2022-09-02 17:31:19 +01:00
b9d01ddadc
summary logger → summarywriter
2022-09-02 17:28:00 +01:00
9f7f4af784
dataset: properly resize
2022-09-02 17:09:38 +01:00
c78b6562ef
debug adjustment
2022-09-02 17:00:21 +01:00
c89677abd7
dataset: explicit reshape
2022-09-02 16:57:59 +01:00
c066ea331b
dataset: namespace → dict
...
Python is so annoying
2022-09-02 16:07:44 +01:00
dbff15d4a9
add bug comment
2022-09-01 19:05:50 +01:00
3e4128c0a8
resize rainfall to be 1/2 size of current
2022-09-01 18:47:07 +01:00
8a86728b54
ai: how did I miss that
2022-09-01 18:07:53 +01:00
b2f1ba29bb
debug a
2022-09-01 18:05:33 +01:00
e2f621e212
even moar debug logging
2022-09-01 17:49:51 +01:00
2663123407
moar logging
2022-09-01 17:40:23 +01:00
c2fcb3b954
ai: channels_first → channels_last
2022-09-01 17:06:18 +01:00
f1d7973f22
ai: add dummy label
2022-09-01 17:01:00 +01:00
17d42fe899
ai: json.dumps
2022-09-01 16:21:52 +01:00
940f7aa1b5
ai: set self.model
2022-09-01 16:20:23 +01:00
f1be5fe2bd
ai: add missing arguments to LossContrastive
2022-09-01 16:14:00 +01:00
ddb375e906
ai: another typo
2022-09-01 16:12:05 +01:00
cb3e1d3a23
ai: fixup
2022-09-01 16:10:50 +01:00
e4c95bc7e3
typo
2022-09-01 16:09:24 +01:00
cfbbe8e8cf
ai: global? really?
2022-09-01 16:06:24 +01:00
4952ead094
ai: try the nonlocal keyword? I'm unsure what's going on here
...
....clearly I need to read up on scoping in python.
2022-09-01 16:02:37 +01:00
8bdded23eb
ai: fix 'nother crash' name ConvNeXt submodels
2022-08-31 18:57:27 +01:00
b2a320134e
ai: typo
2022-08-31 18:54:03 +01:00
e4edc68df5
ai: add missing gamma layer
2022-08-31 18:52:35 +01:00
51cf08a386
ResNetRSV2 → ConvNeXt
...
ironically this makes the model simpler o/
2022-08-31 18:51:01 +01:00
3d614d105b
channels first
2022-08-31 18:06:59 +01:00
654eefd9ca
properly handle water dimensions; add log files to .gitignore
...
TODO: add heightmap
2022-08-31 18:03:39 +01:00
5846828f9e
debug logging
2022-08-31 17:48:09 +01:00
12c77e128d
handle feature_dim properly
2022-08-31 17:41:51 +01:00
c52a9f961c
and another
2022-08-31 17:37:28 +01:00
58dbabd561
fix another crash
2022-08-31 17:33:07 +01:00
dbf929325a
typo; add pretrain slurm job file
2022-08-31 16:32:17 +01:00
f2312c1184
fix crash
2022-08-31 16:25:27 +01:00
15a3519107
ai: the best thing about implementing a model is that you don't have to test it on the same day :P
2022-08-11 18:26:28 +01:00
c0a9cb12d8
ai: start creating initial model implementation.
...
it's not hooked up to the CLI yet though.
Focus is still on ensuring the dataset is in the right format though
2022-08-10 19:03:25 +01:00
b52c7f89a7
Move dataset parsing function to the right place
2022-08-10 17:24:55 +01:00
28a3f578d5
update .gitignore
2022-08-04 16:49:53 +01:00
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
b53c77a2cb
index.py: call static function name run
2022-07-26 19:51:28 +01:00
a7ed58fc03
ai: move requirements.txt to the right place
2022-07-26 19:25:11 +01:00
e93a95f1b3
ai dataset: add if main == main
2022-07-26 19:24:40 +01:00
de4c3dab17
typo
2022-07-26 19:14:55 +01:00
18a7d3674b
ai: create (untested) dataset
2022-07-26 19:14:10 +01:00
dac6919fcd
ai: start creating initial scaffolding
2022-07-25 19:01:10 +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