-
Notifications
You must be signed in to change notification settings - Fork 15
/
todo.tasks
159 lines (145 loc) · 9.08 KB
/
todo.tasks
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
Predator-prey:
☐ Leave off bits of five years for match prediction task
☐ Give inputs and select based on those?
☐ Clean up generator:
☐ Mechanism for unused arguments?
☐ Split off one more abstract class?
☐ Do not use
☐ WBML: predator prey data + generator
http://people.whitman.edu/~hundledr/courses/M250F03/LynxHare.txt
☐ Other data? TODO?
☐ Read model modifications here:
https://jckantor.github.io/CBE30338/02.05-Hare-and-Lynx-Population-Dynamics.html
Union inputs (EEG and synthetic):
☐ `nps.Union((x1, 0), (x2, 1), ...)` for output selection @high
☐ Different x locations and different number in different outputs in data gens. @high
☐ Multiple context sets:
☐ Support multiple contexts in NP, ANP
AR:
☐ Support grid-like inputs in AR functions. @high
☐ AR sampling for multi-dimensional outputs. @high
1D Improvements:
☐ UNet receptive field for sawtooth extrapolation? Decide! @high
☐ Increase lengthscale of weakly-periodic period EQ @high
TODOs:
☐ Noisy samples for CIs
☐ Stabilise masking test
UNet:
☐ Multiple conv blocks:
☐ `unet_skip_connections=(True, True, False, False)`
☐ `unet_striding=(True, True, False, False)`
☐ `unet_repeat_block=(1, 2, 1, 1)`
☐ Even kernel size
Short Term:
☐ Copy README from NeuralProcesses.jl and finish first version @high
☐ Speed up `dim-y >= 2 generation? Necessary? Cache Stheno model?
☐ IW for `loglik`
Refactor:
☐ Integrate transforms with `probmods`.
Features:
☐ kvv covariance
☐ Learnable channel
☐ Gibbs kernel (MLKernels)
☐ `pairwise_from_dist2`
☐ `elwise_from_dist2`
☐ Given initialisation, like PCA
＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿
Archive:
✓ Scaling of outputs in generator @done (22-04-28 16:13) @project(Predator-prey)
✓ Ensure never reaches zero: positive noise @done (22-04-28 16:13) @project(Predator-prey)
✓ Random scaling? @done (22-04-28 16:12) @project(Predator-prey)
✓ Random offset? @done (22-04-28 16:12) @project(Predator-prey)
✓ LAB: @done (22-04-28 16:12) @project(Predator-prey)
✓ Indexing with GPU tensors: converts to NumPy automatically: is there a specialised functions for taking indices? Overload for PyTorch! @done (22-04-28 16:12) @project(Predator-prey)
✓ Indexing int32s @done (22-04-28 16:12) @project(Predator-prey)
✓ Cap maximum. @done (22-04-28 16:12) @project(Predator-prey)
✓ `schedule_gp`: wait until processes finished. @done (22-04-28 14:01) @project(TODO)
✓ Fix up snippet @done (22-04-28 12:16) @project(TODO)
✓ Fix `git diff` locally @done (22-04-28 12:01) @project(Predator-prey)
✓ `predprey.py` @done (22-04-28 11:59) @project(Predator-prey)
✓ Fix `git diff` on server @done (22-04-28 11:59) @project(Predator-prey)
✓ Same number of contexts in mixture generator @high @done (22-04-28 11:38) @project(1D Improvements)
✓ Reconstruct data gen for every eval @done (22-04-28 11:38)
x Train without normalisation, but report with normalisation @critical @cancelled (22-04-27 17:56) @project(Bugs)
✓ Loglik versus ELBO?! What's going on? @critical @done (22-04-27 16:07) @project(Bugs)
✓ Check 2D weakly-periodic samples @critical @done (22-04-27 16:07) @project(Bugs)
✓ `cv_objective` @critical @done (22-04-27 10:28) @project(Bugs)
✓ ELBO ConvNP with loglik objective failing? @critical @done (22-04-27 10:05) @project(Bugs)
✓ Add `ar_elbo`. @high @done (22-04-26 18:10) @project(AR)
x DWS over UNet to enable same settings for all models? @cancelled (22-04-26 09:12) @project(1D Improvements)
More expensive than UNet!
✓ Add `grid` option to `ar_predict`. @done (22-04-25 21:29) @project(AR)
✓ `B.randperm` @done (22-04-25 19:54) @project(AR)
✓ Random states for AR functions @done (22-04-25 19:54) @project(AR)
✓ Implement random order in _sort_targets @done (22-04-25 19:54) @project(AR)
✓ Timing of ConvGNP x2_y2 on sawtooth? @done (22-04-25 18:32) @project(AR)
✓ Everything <= 2 min / epoch @done (22-04-25 14:30) @project(AR)
x Check ConvNP sawtooth x2_y2 fit @cancelled (22-04-25 14:30) @project(AR)
x Check ConvCNP sawtooth x2_y2 fit @cancelled (22-04-25 14:30) @project(AR)
✓ Ability to fix the noise to be small for early epochs? @done (22-04-25 12:22) @project(Short Term)
✓ Make ConvNP 2D outputs fit on sawtooth and weakly-periodic @high @done (22-04-25 12:14) @project(AR)
✓ Check ANP ELBO? @high @done (22-04-25 11:54) @project(AR)
✓ `schedule_gp.py`: ability to ignore runs which already finished! @done (22-04-25 11:49) @project(AR)
✓ Check 2D plotting for CNP: do ConvCNP in same way! @high @done (22-04-25 11:49) @project(AR)
✓ 200 -> 500 in 1d function, remove plt.show() @done (22-04-23 15:40) @project(AR)
✓ Undo sorting of inputs @done (22-04-23 15:22) @project(AR)
✓ ConvNP + x2_y* + loglik? Too memory intensive. @high @done (22-04-23 15:05) @project(TODOs)
Only ELBO or batch/4 rate/4?
Batch size 4 takes 25 min/epoch
ELBO@5 takes 9 min/epoch
Simply exclude ConvNP?
✓ Change folder to first x1_y1 @high @done (22-04-23 15:05) @project(TODOs)
✓ Reduce num contexts for 1D again! @done (22-04-23 14:00) @project(TODOs)
✓ Add LVs option to script @done (22-04-23 14:00) @project(TODOs)
✓ Check ConvGNP on eq and weakly-periodic from epoch 38 @high @done (22-04-23 13:53) @project(TODOs)
x Check ConvGNP on weakly-periodic @high @cancelled (22-04-23 13:42) @project(TODOs)
✓ BatchedMLP allow option `num_layers` and simplify code. @done (22-04-22 16:26) @project(TODOs)
x AR more during eval @cancelled (22-04-22 16:26) @project(TODOs)
✓ Allow dim_y_latent > 1 but dim_y = 1 @done (22-04-22 16:20) @project(TODOs)
✓ seed_parameters @done (22-04-22 16:19) @project(TODOs)
✓ FIX SEED FOR MIXING MATRIX!!!!!! @critical @done (22-04-22 16:19) @project(TODOs)
✓ Plotting intensity? @done (22-04-22 16:17) @project(TODOs)
✓ Show both train and test loss @done (22-04-22 13:01) @project(TODOs)
✓ Add `--evaluate-batch-size` @done (22-04-22 13:01) @project(TODOs)
✓ Remove noise from sawtooth @done (22-04-22 13:00) @project(TODOs)
✓ Context to (0, 50) and (0, 100) @done (22-04-22 13:00) @project(TODOs)
✓ rand for weights @done (22-04-22 11:21) @project(TODOs)
✓ more observations generally? @done (22-04-22 11:21) @project(TODOs)
✓ divide by factor in sawtooth length scale! @done (22-04-22 11:21) @project(TODOs)
64 channels, 32 ppu, 1e-4 fits, UNet!
✓ Get ConvCNP to fit x1_y2 sawtooth: @done (22-04-22 11:21) @project(TODOs)
Only positive weights? Yes, that seems to work.
LR important? No, eventually loss goes crazy again. Well, need 1e-5.
Correlations important? Yes, that seems to stabilise training.
Or is number of observations important? No, but affects loss.
Need many UNet channels? Need at least (128,) * 6 channels.
Need at least PPU 64?
ppu 128, 512 channels, 1e-6
✓ ConvNP x2_y2 eval batch size tuning --evaluate-batch-size @done (22-04-22 11:21) @project(TODOs)
✓ ConvCNP fit x2_y1 sawtooth! @done (22-04-22 11:21) @project(TODOs)
✓ 10 works for eval; 5 works for training @done (22-04-22 11:21) @project(TODOs)
✓ Plotting: @done (22-04-22 09:12) @project(TODOs)
✓ Get ANP to fit! @done (22-04-22 09:12) @project(Sanity Checks)
✓ Does increasing `evaluate_num_samples` indeed help for the LV models? @done (22-04-22 09:12) @project(Sanity Checks)
✓ 2D outputs @done (22-04-22 09:12) @project(TODOs)
✓ 2D inputs @done (22-04-22 09:12) @project(TODOs)
✓ Performance FullConvGNP on `weakly-periodic`? @done (22-04-21 16:51) @project(Sanity Checks)
x `points_per_unit` appropriate or too low? @cancelled (22-04-21 16:51) @project(Sanity Checks)
✓ Performance ConvNP on `sawtooth`? @done (22-04-21 16:35) @project(Sanity Checks)
x `rate` 5e-4 too high? @cancelled (22-04-21 16:35) @project(Sanity Checks)
✓ Carefully configure all settings of all models @done (22-04-21 16:28) @project(TODOs)
✓ Resume training at epoch @done (22-04-21 16:08) @project(TODOs)
✓ Plot a few before running eval @done (22-04-21 15:50) @project(TODOs)
✓ Fix convcnp @done (22-04-21 15:50) @project(TODOs)
✓ Random state in objective @done (22-04-21 15:41) @project(TODOs)
✓ Check number of samples @done (22-04-21 15:29) @project(TODOs)
✓ Check ANP arch with paper @done (22-04-21 15:24) @project(TODOs)
✓ Carefully configure all models: ANP and NP `width` kw? @done (22-04-21 15:24) @project(TODOs)
✓ Fix duplicate code between models @done (22-04-21 15:22) @project(TODOs)
✓ Transforms for all models @done (22-04-21 14:49) @project(Short Term)
✓ Log of eval elsewhere @high @done (22-04-21 14:45) @project(TODOs)
✓ Eval mode for script @high @done (22-04-21 14:29) @project(TODOs)
✓ `loglik` batching for high-number of samples @done (22-04-21 13:18) @project(TODOs)
✓ Port Julia repo @high @done (22-04-21 12:41) @project(TODOs / Port)
✓ GNP @high @done (22-04-21 12:41) @project(TODOs / Port)
https://github.com/wesselb/neuralprocesses/tree/f4de0a3f9c0d4971d29813a6ff9acf763f73f05c/neuralprocesses/gnp