feat(cnn_v3): Phase 5 complete — parity validation passing (36/36 tests)

- Add test_cnn_v3_parity.cc: zero_weights + random_weights tests
- Add gen_test_vectors.py: PyTorch reference implementation for enc0/enc1/bn/dec1/dec0
- Add test_vectors.h: generated C header with enc0, dec1, output expected values
- Fix declare_nodes(): intermediate textures at fractional resolutions (W/2, W/4)
  using new NodeRegistry::default_width()/default_height() getters
- Add layer-by-layer readback (enc0, dec1) for regression coverage
- Final parity: enc0 max_err=1.95e-3, dec1 max_err=1.95e-3, out max_err=4.88e-4

handoff(Claude): CNN v3 parity done. Next: train_cnn_v3.py (FiLM MLP training).

1 files changed, 2 insertions, 1 deletions

diff --git a/TODO.md b/TODO.md
index 11c629e..559f8b3 100644
--- a/TODO.md
+++ b/TODO.md
@@ -76,7 +76,8 @@ PyTorch / HTML WebGPU / C++ WebGPU.
    - Params alignment fix: WGSL `vec3u` align=16 → C++ structs 64/96 bytes
    - Weight offsets as explicit formulas (e.g. `20*4*9+4`)
    - FiLM γ/β: identity defaults; real values require trained MLP (see below)
-5. Parity validation (test vectors, ≤1/255 per pixel)
+5. ✅ Parity validation: test vectors + `test_cnn_v3_parity.cc`. max_err=4.88e-4 (≤1/255).
+   - Key fix: intermediate nodes at fractional resolutions (W/2, W/4) via `NodeRegistry::default_width()/default_height()`
 
 **FiLM MLP training** (blocks meaningful Phase 4 output):
 - Needs `cnn_v3/training/train_cnn_v3.py` — not yet written