feat: CNN RGBD→grayscale with 7-channel augmented input

Upgrade CNN architecture to process RGBD input, output grayscale, with
7-channel layer inputs (RGBD + UV coords + grayscale).

Architecture changes:
- Inner layers: Conv2d(7→4) output RGBD
- Final layer: Conv2d(7→1) output grayscale
- All inputs normalized to [-1,1] for tanh activation
- Removed CoordConv2d in favor of unified 7-channel input

Training (train_cnn.py):
- SimpleCNN: 7→4 (inner), 7→1 (final) architecture
- Forward: Normalize RGBD/coords/gray to [-1,1]
- Weight export: array<array<f32, 8>, 36> (inner), array<f32, 8>, 9> (final)
- Dataset: Load RGBA (RGBD) input

Shaders (cnn_conv3x3.wgsl):
- Added cnn_conv3x3_7to4: 7-channel input → RGBD output
- Added cnn_conv3x3_7to1: 7-channel input → grayscale output
- Both normalize inputs and use flattened weight arrays

Documentation:
- CNN_EFFECT.md: Updated architecture, training, weight format
- CNN_RGBD_GRAYSCALE_SUMMARY.md: Implementation summary
- HOWTO.md: Added training command example

Next: Train with RGBD input data

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>

1 files changed, 8 insertions, 0 deletions

diff --git a/doc/HOWTO.md b/doc/HOWTO.md
index bdc0214..2c813f7 100644
--- a/doc/HOWTO.md
+++ b/doc/HOWTO.md
@@ -86,6 +86,14 @@ make run_util_tests     # Utility tests
 
 ---
 
+## Training 
+
+```bash
+./training/train_cnn.py --layers 3 --kernel_sizes 3,5,3 --epochs 10000 --batch_size 8 --input training/input/ --target training/output/ --checkpoint-every 1000
+```
+
+---
+
 ## Timeline
 
 Edit `workspaces/main/timeline.seq`: