Commit graph

587 commits

Author SHA1 Message Date
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
612735aaae
rename shuffle arg 2022-10-21 16:35:45 +01:00
c98d8d05dd
segmentation: use the right accuracy 2022-10-21 16:17:05 +01:00
bb0258f5cd
flip squeeze operator ordering 2022-10-21 15:38:57 +01:00
af26964c6a
batched_iterator: reset i_item after every time 2022-10-21 15:35:43 +01:00
c5b1501dba
train-predict fixup 2022-10-21 15:27:39 +01:00
42aea7a0cc
plt.close() fixup 2022-10-21 15:23:54 +01:00
12dad3bc87
vis/segmentation: fix titles 2022-10-21 15:22:35 +01:00
0cb2de5d06
train-preedict: close matplotlib after we've finished
they act like file handles
2022-10-21 15:19:31 +01:00
81e53efd9c
PNG: create output dir if doesn't exist 2022-10-21 15:17:39 +01:00
3f7db6fa78
fix embedding confusion 2022-10-21 15:15:59 +01:00
847cd97ec4
fixup 2022-10-21 14:26:58 +01:00
0e814b7e98
Contraster → Segmenter 2022-10-21 14:25:43 +01:00
1b658a1b7c
train-predict: can't destructure array when iterating generator
....it seems to lead to undefined behaviour or something
2022-10-20 19:34:04 +01:00
aed2348a95
train_predict: fixup 2022-10-20 15:42:33 +01:00
cc6679c609
batch data; use generator 2022-10-20 15:22:29 +01:00
d306853c42
use right daataset 2022-10-20 15:16:24 +01:00
59cfa4a89a
basename paths 2022-10-20 15:11:14 +01:00
4d8ae21a45
update cli help text 2022-10-19 17:31:42 +01:00
200076596b
finish train_predict 2022-10-19 17:26:40 +01:00
488f78fca5
pretrain_predict: default to parallel_reads=0 2022-10-19 16:59:45 +01:00
63e909d9fc
datasets: add shuffle=True/False to get_filepaths.
This is important because otherwise it SCAMBLES the filenames, which is a disaster for making predictions in the right order....!
2022-10-19 16:52:07 +01:00
fe43ddfbf9
start implementing driver for train_predict, but not finished yet 2022-10-18 19:37:55 +01:00
4ceec73e5b
Merge branch 'main' of git.starbeamrainbowlabs.com:sbrl/PhD-Rainfall-Radar 2022-10-18 19:07:23 +01:00
0c11ddca4b
rainfallwrangler does NOT mess up the ordering of the data 2022-10-18 19:07:14 +01:00
b3ea189d37
segmentation: softmax the output 2022-10-13 21:02:57 +01:00
f121bfb981
fixup summaryfile 2022-10-13 17:54:42 +01:00
5c35c0cee4
model_segmentation: document; remove unused args 2022-10-13 17:50:16 +01:00
f12e6ab905
No need for a CLI arg for feature_dim_in - metadata should contain this 2022-10-13 17:37:16 +01:00