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7 hours
Streamline CNN v2 training pipeline output
skal
7 hours
Fix CNN v2 training: always save final checkpoint, derive num_layers
skal
7 hours
Fix --mix option: blend prev layer with static p4-p7, not p0-p3
skal
7 hours
Fix CNN v2 static feature channel mapping (p4-p7 ā channels 8-11)
skal
8 hours
gen_identity_weights: Change --mix to 50-50 blend
skal
8 hours
gen_identity_weights: Add --p47 option for static feature visualization
skal
8 hours
gen_identity_weights: Add --mix option for static feature blending
skal
9 hours
CNN v2: Fix Layer 0 visualization scale (was 0.5, now 1.0)
skal
9 hours
CNN v2: Add debugging tools for mismatch investigation
skal
9 hours
CNN v2 training: Fix float64/float32 dtype mismatch in depth feature
skal
9 hours
CNN v2: Alpha channel depth handling and layer visualization
skal
10 hours
CNN v2: Use alpha channel for p3 depth feature + layer visualization
skal
15 hours
CNN v2 training: Add --grayscale-loss option for luminance-based loss computa...
skal
15 hours
CNN v2: Change feature #6 from sin(10*x) to sin(20*y)
skal
15 hours
CNN v2: Add TODO for flexible feature layout in binary format v3
skal
16 hours
CNN v2: Add mip-level support to runtime effect
skal
16 hours
CNN v2 export: Read and display mip_level from checkpoints
skal
16 hours
CNN v2: Add --mip-level option for parametric features
skal
16 hours
CNN v2: Fix activation function mismatch between training and inference
skal
20 hours
CNN v2 training: Use target image alpha channel
skal
20 hours
CNN v2: Restore per-layer kernel sizes support
skal
20 hours
CNN v2: Refactor to uniform 12Dā4D architecture
skal
24 hours
Add weights/ subdirectory to workspaces for CNN training outputs
skal
41 hours
test_demo: Add beat-synchronized CNN post-processing with version selection
skal
44 hours
Refine training script output and validation
skal
44 hours
TODO: 8-bit weight quantization for 2Ć size reduction
skal
44 hours
CNN v2: Storage buffer complete - real weights exported
skal
44 hours
CNN v2: storage buffer architecture foundation
skal
45 hours
TODO: Add random sampling to patch-based training
skal
45 hours
CNN v2: Patch-based training as default (like CNN v1)
skal
45 hours
Fix: CNN v2 training - handle variable image sizes
skal
45 hours
CNN v2: parametric static features - Phases 1-4
skal
47 hours
remove more stale files
skal
2 days
feat: implement beat-based timing system
skal
2 days
add trained layers
skal
2 days
docs: Update CNN comments and add bias fix summary
skal
2 days
fix: CNN bias accumulation and output format improvements
skal
3 days
update cnn code
skal
3 days
refactor: Use linspace(-1,1) directly for coords
skal
3 days
fix: Compute gray from [0,1] RGB in CNN shader generator
skal
3 days
fix: Complete auxiliary texture initialization fix
skal
3 days
add --save-intermediates to train.py and cnn_test
skal
3 days
fix: Move sigmoid activation to call site in CNN layer shader
skal
3 days
fix: Replace clamp with sigmoid in CNN final layer
skal
3 days
feat: Add early stopping to CNN training
skal
3 days
fix: CNN training/inference to match WGSL sliding window
skal
3 days
format .wgsl layer code (cosmetics)
skal
3 days
fix: Use patch-based inference to match CNN training distribution
skal
3 days
opt: Move invariant in1 calculation outside CNN convolution loops
skal
3 days
opt: Vec4-optimize CNN convolution shaders for SIMD
skal
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