fix(cnn_v3): remove dec0 ReLU, load FiLM MLP at runtime

Two bugs blocking training convergence:

1. dec0 ReLU before sigmoid constrained output to [0.5,1.0] — network
   could never produce dark pixels. Removed F.relu in train_cnn_v3.py
   and max(0,…) in cnn_v3_dec0.wgsl. Test vectors regenerated.

2. set_film_params() used hardcoded heuristics instead of the trained MLP.
   Added CNNv3FilmMlp struct + load_film_mlp() to cnn_v3_effect.h/.cc.
   MLP auto-loaded from ASSET_WEIGHTS_CNN_V3_FILM_MLP at construction;
   Linear(5→16)→ReLU→Linear(16→72) runs CPU-side each frame.

36/36 tests pass. Parity max_err=4.88e-4 unchanged.

handoff(Gemini): retrain from scratch — needs ≥50 samples (currently 11).
See cnn_v3/docs/HOWTO.md §2-3.

1 files changed, 6 insertions, 2 deletions

diff --git a/cnn_v3/docs/CNN_V3.md b/cnn_v3/docs/CNN_V3.md
index a197a1d..081adf8 100644
--- a/cnn_v3/docs/CNN_V3.md
+++ b/cnn_v3/docs/CNN_V3.md
@@ -19,7 +19,7 @@ CNN v3 is a next-generation post-processing effect using:
 - Training from both Blender renders and real photos
 - Strict test framework: per-pixel bit-exact validation across all implementations
 
-**Status:** Phases 1–7 complete. Architecture upgraded to enc_channels=[8,16] for improved capacity. Parity test and runtime updated. Next: training pass.
+**Status:** Phases 1–9 complete. Architecture upgraded to enc_channels=[8,16]. Two training bugs fixed (dec0 ReLU removed; FiLM MLP loaded at runtime). Parity validated. Next: retrain from scratch with more data.
 
 ---
 
@@ -34,9 +34,13 @@ FiLM is applied **inside each encoder/decoder block**, after each convolution.
 ### U-Net Block (per level)
 
 ```
-input → Conv 3×3 → BN (or none) → FiLM(γ,β) → ReLU → output
+enc0/enc1/dec1:  input → Conv 3×3 → FiLM(γ,β) → ReLU → output
+dec0 (final):    input → Conv 3×3 → FiLM(γ,β) → Sigmoid → output
 ```
 
+The final decoder layer uses sigmoid directly — **no ReLU** — so the network
+can output the full [0,1] range. ReLU before sigmoid would clamp to [0.5,1.0].
+
 FiLM at level `l`:
 ```
 FiLM(x, γ_l, β_l) = γ_l ⊙ x + β_l      (per-channel affine)