demo.git/training, branch main

remove old files, add new training set

2026-02-27T22:59:49Z

refactor(cnn): rename cnn_effect to cnn_v1_effect for clarity

2026-02-15T17:55:42Z

Renamed files and classes: - cnn_effect.{h,cc} → cnn_v1_effect.{h,cc} - CNNEffect → CNNv1Effect - CNNEffectParams → CNNv1EffectParams - CNNLayerParams → CNNv1LayerParams - CNN_EFFECT.md → CNN_V1_EFFECT.md Updated all references: - C++ includes and class usage - CMake source list - Timeline (workspaces/main/timeline.seq) - Test file (test_demo_effects.cc) - Documentation (CLAUDE.md, PROJECT_CONTEXT.md, READMEs) Tests: 34/34 passing (100%)

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

2026-02-15T17:52:48Z

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%)

refactor(cnn): isolate CNN v2 to cnn_v2/ subdirectory

2026-02-15T17:44:17Z

Move all CNN v2 files to dedicated cnn_v2/ directory to prepare for CNN v3 development. Zero functional changes. Structure: - cnn_v2/src/ - C++ effect implementation - cnn_v2/shaders/ - WGSL shaders (6 files) - cnn_v2/weights/ - Binary weights (3 files) - cnn_v2/training/ - Python training scripts (4 files) - cnn_v2/scripts/ - Shell scripts (train_cnn_v2_full.sh) - cnn_v2/tools/ - Validation tools (HTML) - cnn_v2/docs/ - Documentation (4 markdown files) Changes: - Update CMake source list to cnn_v2/src/cnn_v2_effect.cc - Update assets.txt with relative paths to cnn_v2/ - Update includes to ../../cnn_v2/src/cnn_v2_effect.h - Add PROJECT_ROOT resolution to Python/shell scripts - Update doc references in HOWTO.md, TODO.md - Add cnn_v2/README.md Verification: 34/34 tests passing, demo runs correctly. Co-Authored-By: Claude Sonnet 4.5

CNN v2: bilinear mip-level sampling and UI improvements

2026-02-14T06:24:51Z

**CNN v2 Changes:** - Replace point sampling with bilinear interpolation for mip-level features - Add linear sampler (binding 6) to static features shader - Update CNNv2Effect, cnn_test, and HTML tool **HTML Tool UI:** - Move controls to floating bottom bar in central view - Consolidate video controls + Blend/Depth/Save PNG in single container - Increase left panel width: 300px → 315px (+5%) - Remove per-frame debug messages (visualization, rendering logs) **Technical:** - WGSL: textureSample() with linear_sampler vs textureLoad() - C++: Create WGPUSampler with Linear filtering - HTML: Change sampler from 'nearest' to 'linear' handoff(Claude): CNN v2 now uses bilinear mip-level sampling across all tools

Replace hard clamp with sigmoid activation in CNN v2

2026-02-14T01:12:12Z

Fixes training collapse where p1/p2 channels saturate due to gradient blocking at clamp boundaries. Sigmoid provides smooth [0,1] mapping with continuous gradients. Changes: - Layer 0: clamp(x, 0, 1) → sigmoid(x) - Final layer: clamp(x, 0, 1) → sigmoid(x) - Middle layers: ReLU unchanged (already stable) Updated files: - training/train_cnn_v2.py: PyTorch model activations - workspaces/main/shaders/cnn_v2/cnn_v2_compute.wgsl: WGSL shader - tools/cnn_v2_test/index.html: HTML validation tool - doc/CNN_V2.md: Documentation Validation: - Build clean (no shader errors) - 34/36 tests pass (2 unrelated script tests fail) - 10-epoch training: loss 0.153 → 0.088 (good convergence) - cnn_test processes images successfully Breaking change: Old checkpoints trained with clamp() incompatible. Retrain from scratch required. handoff(Claude): CNN v2 sigmoid activation implemented and validated.

Streamline CNN v2 training pipeline output

2026-02-14T00:36:30Z

- Add --quiet flag to export script (single-line summary) - Compact validation output (all images on one line) - Reduce noise: export 3 layers, 912 weights, 1904 bytes Co-Authored-By: Claude Sonnet 4.5

Fix CNN v2 training: always save final checkpoint, derive num_layers

2026-02-14T00:33:37Z

- Always save final checkpoint after training completes - Derive num_layers from kernel_sizes list when multiple values provided - Add checkpoint validation in training pipeline script - Quote shell variables when passing args to Python Fixes issue where no checkpoint saved when epochs < checkpoint_every. Co-Authored-By: Claude Sonnet 4.5

Fix --mix option: blend prev layer with static p4-p7, not p0-p3

2026-02-14T00:04:07Z

Updated gen_identity_weights.py --mix mode to use static features p4-p7 (uv_x, uv_y, sin20_y, bias) at channels 8-11 instead of p0-p3 (RGB+D) at channels 4-7. Before: 0.5*prev[i] + 0.5*static_p{i} (channels 4-7) After: 0.5*prev[i] + 0.5*static_p{4+i} (channels 8-11) Co-Authored-By: Claude Sonnet 4.5

Fix CNN v2 static feature channel mapping (p4-p7 → channels 8-11)

2026-02-14T00:00:52Z

Fixed bug in gen_identity_weights.py --p47 mode: static features p4-p7 (uv_x, uv_y, sin20_y, bias) are at input channels 8-11, not 4-7. Weight tensor layout: - Channels 0-3: Previous layer output (4D RGBA) - Channels 4-11: Static features (8D: p0-p7) Static features: - p0-p3 (channels 4-7): RGB+D from mip level - p4-p7 (channels 8-11): uv_x, uv_y, sin20_y, bias Updated: - training/gen_identity_weights.py: Change weights[i,i+4] to weights[i,i+8] - workspaces/main/weights/mix_p47.bin: Regenerated (not in repo) - doc/CNN_V2.md: Add Input Channel Mapping section with full layout table Co-Authored-By: Claude Sonnet 4.5