archive(cnn): move CNN v1 to cnn_v1/ subdirectory

Consolidate CNN v1 (CNNEffect) into dedicated directory:
- C++ effect: src/effects → cnn_v1/src/
- Shaders: workspaces/main/shaders/cnn → cnn_v1/shaders/
- Training: training/train_cnn.py → cnn_v1/training/
- Docs: doc/CNN*.md → cnn_v1/docs/

Updated all references:
- CMake source list
- C++ includes (relative paths: ../../cnn_v1/src/)
- Asset paths (../../cnn_v1/shaders/)
- Documentation cross-references

CNN v1 remains active in timeline. For new work, use CNN v2 with
enhanced features (7D static, storage buffer, sigmoid activation).

Tests: 34/34 passing (100%)

1 files changed, 0 insertions, 85 deletions

diff --git a/doc/CNN_BIAS_FIX_2026-02.md b/doc/CNN_BIAS_FIX_2026-02.md
deleted file mode 100644
index 26db8eb..0000000
--- a/doc/CNN_BIAS_FIX_2026-02.md
+++ /dev/null
@@ -1,85 +0,0 @@
-# CNN Bias Accumulation Fix (2026-02-11)
-
-## Problem
-Bias was being added multiple times in shader convolution loops (once per kernel position), causing mismatch between PyTorch training and WGSL inference.
-
-## Root Cause
-**Location**: `training/train_cnn.py:381, 398`
-
-When exporting weights to WGSL, bias was replicated for every kernel position. The shader loops through positions doing:
-```wgsl
-sum += dot(weights[pos], rgbd) + dot(weights[pos+1], in1);  // in1.w = 1.0
-```
-
-For 3×3 kernel (9 positions), bias added 9×. For 5×5, added 25×.
-
-## Fix
-Divide bias by `num_positions` during export:
-```python
-# Final layer (7→1)
-v1.append(f"{bias[0] / num_positions:.6f}")
-
-# Inner layers (7→4)
-v1.append(f"{bias[out_c] / num_positions:.6f}")
-```
-
-Shader accumulates bias × num_positions = original bias (correct).
-
----
-
-## Additional Improvements
-
-### 1. RGBA Output Support
-**train_cnn.py**: Now saves 4-channel RGBA PNG preserving alpha from input:
-```python
-alpha = img_tensor[0, 3:4, :, :].permute(1, 2, 0).numpy()
-output_rgba = np.concatenate([output, alpha], axis=2)
-Image.fromarray((output_rgba * 255).astype(np.uint8), mode='RGBA')
-```
-
-Intermediate layers also save RGBA if 4-channel.
-
-### 2. Debug Hex Output
-**Both tools** support `--debug-hex` to print first 8 pixels as hex:
-```bash
-./training/train_cnn.py --infer input.png --export-only checkpoint.pth --debug-hex
-./build/cnn_test input.png output.png --debug-hex
-```
-
-Output format: `[0] 0xRRGGBBAA` for pixel-level comparison.
-
-### 3. Cleanup
-Removed sRGB/linear_png debug code from `cnn_test.cc` (simplified PNG saving).
-
----
-
-## Files Modified
-- `training/train_cnn.py`: Bias fix, RGBA output, --debug-hex
-- `tools/cnn_test.cc`: --debug-hex, remove linear_png
-- `workspaces/main/shaders/cnn/cnn_weights_generated.wgsl`: Regenerated with fixed bias
-
-## Testing
-```bash
-# Train with fixed export
-./training/train_cnn.py --input training/input/ --target training/output/ \
-  --layers 3 --kernel_sizes 3,3,3 --epochs 5000
-
-# Generate ground truth
-./training/train_cnn.py --infer input.png --export-only checkpoint.pth \
-  --output ground_truth.png --debug-hex
-
-# Run GPU tool
-./build/cnn_test input.png tool_output.png --debug-hex
-
-# Compare hex output for first 8 pixels
-```
-
----
-
-## Status
-✅ Bias accumulation bug fixed
-✅ RGBA output with alpha preservation
-✅ Debug hex comparison tool
-✅ Weights regenerated
-
-Commit: `8ff8c56`