geolip-conduit-experiments / cell_6_output_claude_annoying_me_ignoring_own_rules.txt
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Loading Fresnel (v50_fresnel_64) + CIFAR-10...
v50_fresnel_64/checkpoints/best.pt: 100%
 23.6M/23.6M [00:01<00:00, 43.7MB/s]
Patch size: 4, Grid: 16x16, D=4
Params: 1,964,643
Fresnel S-value profile on CIFAR-10 sample:
S mean: [5.003195285797119, 3.4307751655578613, 2.7023839950561523, 2.140519142150879]
S std: [0.12345327436923981, 0.13213300704956055, 0.161136656999588, 0.1278010755777359]
MSE: 0.000000
Precomputing train set...
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Train: 96%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹| 377/391 [00:07<00:00, 50.35it/s]
Train: 98%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š| 383/391 [00:07<00:00, 50.35it/s]
Train: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 391/391 [00:07<00:00, 49.23it/s]
Train: 50000 images
Precomputing test set...
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Test: 1%|▏ | 1/79 [00:00<00:09, 8.53it/s]
Test: 8%|β–Š | 6/79 [00:00<00:02, 31.11it/s]
Test: 15%|β–ˆβ–Œ | 12/79 [00:00<00:01, 40.31it/s]
Test: 22%|β–ˆβ–ˆβ– | 17/79 [00:00<00:01, 43.82it/s]
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Test: 51%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆ | 40/79 [00:00<00:00, 49.00it/s]
Test: 58%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š | 46/79 [00:01<00:00, 49.36it/s]
Test: 66%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 52/79 [00:01<00:00, 49.59it/s]
Test: 73%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž | 58/79 [00:01<00:00, 49.77it/s]
Test: 81%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ | 64/79 [00:01<00:00, 49.90it/s]
Test: 87%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 69/79 [00:01<00:00, 49.84it/s]
Test: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 79/79 [00:01<00:00, 45.59it/s] Test: 10000 images
Fresnel signal profile:
S : mean=3.3209 std=1.0780 min=1.8129 max=5.3177
friction : mean=43.0709 std=6270.8301 min=4.6188 max=25124204.0000
settle : mean=1.6777 std=0.7668 min=0.0000 max=4.0000
error : mean=0.0000 std=0.0000 min=0.0000 max=0.0002
======================================================================
FRESNEL β€” Spatial Conv Readout β€” All Conduit Configurations
======================================================================
──────────────────────────────────────────────────────────────────────
Training: Eigenvalues (S) only β€” 4ch
──────────────────────────────────────────────────────────────────────
ep 5 train=32.1% test=34.8%
ep 10 train=37.0% test=38.3%
ep 15 train=39.9% test=40.7%
ep 20 train=41.6% test=42.4%
ep 25 train=42.9% test=43.0%
ep 30 train=43.3% test=43.5%
Eigenvalues (S) only β€” 4ch
Channels: 4, Params: 232,714, Time: 29s
Best test: 43.6%
Class Acc
----------------------
airplane 46.1% β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ
auto 56.8% β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ
bird 32.4% β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ
cat 23.4% β–ˆβ–ˆβ–ˆβ–ˆ
deer 29.6% β–ˆβ–ˆβ–ˆβ–ˆβ–ˆ
dog 36.6% β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ
frog 52.7% β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ
horse 44.7% β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ
ship 62.0% β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ
truck 51.1% β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ
──────────────────────────────────────────────────────────────────────
Training: Friction only β€” 4ch
──────────────────────────────────────────────────────────────────────
ep 5 train=23.4% test=24.0%
ep 10 train=30.1% test=30.4%
ep 15 train=35.4% test=36.1%
ep 20 train=38.8% test=38.5%
ep 25 train=40.5% test=40.5%
ep 30 train=41.1% test=41.1%
Friction only β€” 4ch
Channels: 4, Params: 232,714, Time: 30s
Best test: 41.2%
Class Acc
----------------------
airplane 44.6% β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ
auto 56.5% β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ
bird 25.9% β–ˆβ–ˆβ–ˆβ–ˆβ–ˆ
cat 22.2% β–ˆβ–ˆβ–ˆβ–ˆ
deer 35.4% β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ
dog 37.6% β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ
frog 50.0% β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ
horse 43.7% β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ
ship 52.7% β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ
truck 42.4% β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ
──────────────────────────────────────────────────────────────────────
Training: Release error only β€” 1ch
──────────────────────────────────────────────────────────────────────
ep 5 train=9.9% test=10.0%
ep 10 train=9.9% test=10.0%
ep 15 train=9.9% test=10.0%
ep 20 train=9.9% test=10.0%
ep 25 train=9.9% test=10.0%
ep 30 train=10.0% test=10.0%
Release error only β€” 1ch
Channels: 1, Params: 230,986, Time: 29s
Best test: 10.0%
Class Acc
----------------------
airplane 0.0%
auto 0.0%
bird 0.0%
cat 0.0%
deer 100.0% β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ
dog 0.0%
frog 0.0%
horse 0.0%
ship 0.0%
truck 0.0%
──────────────────────────────────────────────────────────────────────
Training: Settle only β€” 4ch
──────────────────────────────────────────────────────────────────────
ep 5 train=31.4% test=31.7%
ep 10 train=35.3% test=34.3%
ep 15 train=37.6% test=36.4%
ep 20 train=39.0% test=38.4%
ep 25 train=39.9% test=39.0%
ep 30 train=40.4% test=39.2%
Settle only β€” 4ch
Channels: 4, Params: 232,714, Time: 28s
Best test: 39.2%
Class Acc
----------------------
airplane 32.4% β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ
auto 45.3% β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ
bird 30.5% β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ
cat 18.2% β–ˆβ–ˆβ–ˆ
deer 30.9% β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ
dog 37.0% β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ
frog 53.7% β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ
horse 43.1% β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ
ship 53.8% β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ
truck 46.7% β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ
──────────────────────────────────────────────────────────────────────
Training: S + Friction β€” 8ch
──────────────────────────────────────────────────────────────────────
ep 5 train=28.0% test=29.9%
ep 10 train=34.8% test=35.6%
ep 15 train=39.8% test=40.2%
ep 20 train=42.8% test=42.5%
ep 25 train=44.9% test=44.2%
ep 30 train=45.6% test=44.6%
S + Friction β€” 8ch
Channels: 8, Params: 235,018, Time: 31s
Best test: 44.7%
Class Acc
----------------------
airplane 47.0% β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ
auto 56.4% β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ
bird 27.8% β–ˆβ–ˆβ–ˆβ–ˆβ–ˆ
cat 23.3% β–ˆβ–ˆβ–ˆβ–ˆ
deer 37.9% β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ
dog 40.4% β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ
frog 55.9% β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ
horse 52.4% β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ
ship 56.7% β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ
truck 48.5% β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ
──────────────────────────────────────────────────────────────────────
Training: S + Release error β€” 5ch
──────────────────────────────────────────────────────────────────────
ep 5 train=31.2% test=33.2%
ep 10 train=37.3% test=38.5%
ep 15 train=40.0% test=40.8%
ep 20 train=41.9% test=42.2%
ep 25 train=43.1% test=43.1%
ep 30 train=43.4% test=43.4%
S + Release error β€” 5ch
Channels: 5, Params: 233,290, Time: 30s
Best test: 43.5%
Class Acc
----------------------
airplane 47.1% β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ
auto 56.4% β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ
bird 31.6% β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ
cat 22.4% β–ˆβ–ˆβ–ˆβ–ˆ
deer 30.0% β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ
dog 37.4% β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ
frog 52.9% β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ
horse 44.3% β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ
ship 61.3% β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ
truck 50.3% β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ
──────────────────────────────────────────────────────────────────────
Training: S + Friction + Release β€” 9ch
──────────────────────────────────────────────────────────────────────
ep 5 train=28.7% test=29.8%
ep 10 train=36.2% test=38.0%
ep 15 train=40.9% test=41.9%
ep 20 train=43.8% test=43.7%
ep 25 train=45.8% test=44.9%
ep 30 train=46.4% test=45.1%
S + Friction + Release β€” 9ch
Channels: 9, Params: 235,594, Time: 32s
Best test: 45.1%
Class Acc
----------------------
airplane 50.3% β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ
auto 54.0% β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ
bird 31.8% β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ
cat 23.2% β–ˆβ–ˆβ–ˆβ–ˆ
deer 36.7% β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ
dog 39.2% β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ
frog 58.3% β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ
horse 51.4% β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ
ship 57.3% β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ
truck 49.3% β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ
──────────────────────────────────────────────────────────────────────
Training: FULL CONDUIT β€” 13ch
──────────────────────────────────────────────────────────────────────
ep 5 train=27.1% test=28.7%
ep 10 train=34.3% test=35.6%
ep 15 train=39.1% test=39.9%
ep 20 train=41.9% test=42.2%
ep 25 train=43.6% test=43.5%
ep 30 train=44.3% test=43.9%
FULL CONDUIT β€” 13ch
Channels: 13, Params: 237,898, Time: 32s
Best test: 44.0%
Class Acc
----------------------
airplane 49.1% β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ
auto 56.1% β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ
bird 28.4% β–ˆβ–ˆβ–ˆβ–ˆβ–ˆ
cat 22.1% β–ˆβ–ˆβ–ˆβ–ˆ
deer 36.5% β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ
dog 39.2% β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ
frog 50.2% β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ
horse 49.6% β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ
ship 57.6% β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ
truck 49.7% β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ
======================================================================
SCOREBOARD β€” Fresnel (v50_fresnel_64) Spatial Conv Readout
======================================================================
Configuration Ch Params Test Acc
--------------------------------------------------------------
Chance β€” β€” 10.0%
Release error only β€” 1ch 1 230,986 10.0%
Settle only β€” 4ch 4 232,714 39.2%
Friction only β€” 4ch 4 232,714 41.2%
S + Release error β€” 5ch 5 233,290 43.5%
Eigenvalues (S) only β€” 4ch 4 232,714 43.6%
FULL CONDUIT β€” 13ch 13 237,898 44.0%
S + Friction β€” 8ch 8 235,018 44.7%
S + Friction + Release β€” 9ch 9 235,594 45.1%
--- FRECKLES REFERENCE ---
Scatter + conv (Freckles S) 4 2.9M 70.5%
Fresnel S-only: 43.6%
Best conduit: 45.1% (S + Friction + Release β€” 9ch)
Conduit lift: +1.6pp
KEY QUESTION: Does Fresnel's clean training produce
conduit signals that Freckles' noise training could not?