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, 85 insertions, 0 deletions

diff --git a/cnn_v1/docs/CNN_BIAS_FIX_2026-02.md b/cnn_v1/docs/CNN_BIAS_FIX_2026-02.md
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+# 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`