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| author | skal <pascal.massimino@gmail.com> | 2026-03-25 10:05:42 +0100 |
|---|---|---|
| committer | skal <pascal.massimino@gmail.com> | 2026-03-25 10:05:42 +0100 |
| commit | ce6e5b99f26e4e7c69a3cacf360bd0d492de928c (patch) | |
| tree | a8d64b33a7ea1109b6b7e1043ced946cac416756 /cnn_v3/src | |
| parent | 8b4d7a49f038d7e849e6764dcc3abd1e1be01061 (diff) | |
feat(cnn_v3): 3×3 dilated bottleneck + Sobel loss + FiLM warmup + architecture PNG
- Replace 1×1 pointwise bottleneck with Conv(8→8, 3×3, dilation=2):
effective RF grows from ~13px to ~29px at ¼res (~+1 KB weights)
- Add Sobel edge loss in training (--edge-loss-weight, default 0.1)
- Add FiLM 2-phase training: freeze MLP for warmup epochs then
unfreeze at lr×0.1 (--film-warmup-epochs, default 50)
- Update weight layout: BN 72→584 f16, total 1964→2476 f16 (4952 B)
- Cascade offsets in C++ effect, JS tool, export/gen_test_vectors scripts
- Regenerate test_vectors.h (1238 u32); parity max_err=9.77e-04
- Generate dark-theme U-Net+FiLM architecture PNG (gen_architecture_png.py)
- Replace ASCII art in CNN_V3.md and HOW_TO_CNN.md with PNG embed
handoff(Gemini): bottleneck dilation + Sobel loss + FiLM warmup landed.
Next: run first real training pass (see cnn_v3/docs/HOWTO.md §3).
Diffstat (limited to 'cnn_v3/src')
| -rw-r--r-- | cnn_v3/src/cnn_v3_effect.cc | 2 |
1 files changed, 1 insertions, 1 deletions
diff --git a/cnn_v3/src/cnn_v3_effect.cc b/cnn_v3/src/cnn_v3_effect.cc index bfbb17b..1391eba 100644 --- a/cnn_v3/src/cnn_v3_effect.cc +++ b/cnn_v3/src/cnn_v3_effect.cc @@ -25,7 +25,7 @@ // static const uint32_t kEnc0Weights = 20 * 4 * 9 + 4; // Conv(20→4,3×3)+bias static const uint32_t kEnc1Weights = 4 * 8 * 9 + 8; // Conv(4→8,3×3)+bias -static const uint32_t kBnWeights = 8 * 8 * 1 + 8; // Conv(8→8,1×1)+bias +static const uint32_t kBnWeights = 8 * 8 * 9 + 8; // Conv(8→8,3×3,dilation=2)+bias static const uint32_t kDec1Weights = 16 * 4 * 9 + 4; // Conv(16→4,3×3)+bias static const uint32_t kDec0Weights = 8 * 4 * 9 + 4; // Conv(8→4,3×3)+bias |