Commit graph

263 commits

Author SHA1 Message Date
bf2f6e9b64
debug logging
it begins again
2022-11-11 18:31:40 +00:00
481eeb3759
mono: fix dataset preprocessing
rogue dimension
2022-11-11 18:31:27 +00:00
9035450213
mono: instantiate right model 2022-11-11 18:28:29 +00:00
69a2d0cf04
fixup 2022-11-11 18:27:01 +00:00
65e801cf28
train_mono: fix crash 2022-11-11 18:26:25 +00:00
8ac5159adc
dataset_mono: simplify param passing, onehot+threshold water depth data 2022-11-11 18:23:50 +00:00
3a3f7e85da
typo 2022-11-11 18:03:09 +00:00
3313f77c88
Add (untested) mono rainfall → water depth model
* sighs *
Unfortunately I can't seem to get contrastive learning to work.....
2022-11-10 22:36:11 +00:00
ce194d9227
slurm: customise log file names 2022-11-10 21:09:34 +00:00
9384b89165
model_segmentation: spare → normal crossentropy, activation functions at end 2022-11-10 20:53:37 +00:00
b6676e7361
switch from sparse to normal crossentropy 2022-11-10 20:50:56 +00:00
d8be26d476
Merge branch 'main' of git.starbeamrainbowlabs.com:sbrl/PhD-Rainfall-Radar 2022-11-10 20:49:01 +00:00
b03388de60
dataset_segmenter: DEBUG: fix water shape 2022-11-10 20:48:21 +00:00
daf691bf43
typo 2022-11-10 19:55:00 +00:00
0aa2ce19f5
read_metadata: support file inputs as well as dirs 2022-11-10 19:53:30 +00:00
aa7d9b8cf6
fixup 2022-11-10 19:46:09 +00:00
0894bd09e8
train_predict: add error message for parrams.json not found 2022-11-10 19:45:41 +00:00
0353072d15
allow pretrain to run on gpu
we've slashed the size of the 2nd encoder, so ti should fit naow?
2022-11-04 17:02:07 +00:00
44ad51f483
CallbackNBatchCsv: bugfix .sort() → sorted() 2022-11-04 16:40:21 +00:00
4dddcfcb42
pretrain_predict: missing \n 2022-11-04 16:01:28 +00:00
1375201c5f
CallbackNBatchCsv: open_handle mode 2022-11-03 18:29:00 +00:00
3206d6b7e7
slurm: rename segmenter job name 2022-11-03 17:12:27 +00:00
f2ae74ce7b
how could I be so stupid..... round 2 2022-11-02 17:38:26 +00:00
5f8d6dc6ea
Add metrics every 64 batches
this is important, because with large batches it can be difficult to tell what's happening inside each epoch.
2022-10-31 19:26:10 +00:00
cf872ef739
how could I be so *stupid*...... 2022-10-31 18:40:58 +00:00
da32d75778
make_callbacks: display steps, not samples 2022-10-31 18:36:28 +00:00
dfef7db421
moar debugging 2022-10-31 18:26:34 +00:00
172cf9d8ce
tweak 2022-10-31 18:19:43 +00:00
dbe35ee943
loss: comment l2 norm 2022-10-31 18:09:03 +00:00
5e60319024
fixup 2022-10-31 17:56:49 +00:00
b986b069e2
debug party time 2022-10-31 17:50:29 +00:00
458faa96d2
loss: fixup 2022-10-31 17:18:21 +00:00
55dc05e8ce
contrastive: comment weights that aren't needed 2022-10-31 16:26:48 +00:00
33391eaf16
train_predict/jsonl: don't argmax
I'm interested inthe raw values
2022-10-26 17:21:19 +01:00
74f2cdb900
train_predict: .list() → .tolist() 2022-10-26 17:12:36 +01:00
4f9d543695
train_predict: don't pass model_code
it's redundant
2022-10-26 17:11:36 +01:00
1b489518d0
segmenter: add LayerStack2Image to custom_objects 2022-10-26 17:05:50 +01:00
48ae8a5c20
LossContrastive: normalise features as per the paper 2022-10-26 16:52:56 +01:00
843cc8dc7b
contrastive: rewrite the loss function.
The CLIP paper *does* kinda make sense I think
2022-10-26 16:45:45 +01:00
fad1399c2d
convnext: whitespace 2022-10-26 16:45:20 +01:00
1d872cb962
contrastive: fix initial temperature value
It should be 1/0.07, but we had it set to 0.07......
2022-10-26 16:45:01 +01:00
f994d449f1
Layer2Image: fix 2022-10-25 21:32:17 +01:00
6a29105f56
model_segmentation: stack not reshape 2022-10-25 21:25:15 +01:00
98417a3e06
prepare for NCE loss
.....but Tensorflow's implementation looks to be for supervised models :-(
2022-10-25 21:15:05 +01:00
bb0679a509
model_segmentation: don't softmax twice 2022-10-25 21:11:48 +01:00
f2e2ca1484
model_contrastive: make water encoder significantly shallower 2022-10-24 20:52:31 +01:00
a6b07a49cb
count water/nowater pixels in Jupyter Notebook 2022-10-24 18:05:34 +01:00
a8b101bdae
dataset_predict: add shape_water_desired 2022-10-24 18:05:13 +01:00
587c1dfafa
train_predict: revamp jsonl handling 2022-10-21 16:53:08 +01:00
8195318a42
SparseCategoricalAccuracy: losses → metrics 2022-10-21 16:51:20 +01:00