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revised autokem model

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minjaesong
2026-03-08 20:34:45 +09:00
parent 39603d897b
commit 244371aa9d
8 changed files with 774 additions and 235 deletions
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Autokem/CLAUDE.md
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@@ -25,20 +25,32 @@ make clean
- `apply` creates `.bak` backup, runs inference per cell, writes Y+5 (lowheight) and Y+6 (kern data) pixels. Skips cells with width=0, writeOnTop, or compiler directives
- Model file `autokem.safetensors` must be in the working directory
### PyTorch training (faster prototyping)
```bash
cd Autokem
.venv/bin/python train_torch.py # train with defaults
.venv/bin/python train_torch.py --epochs 300 # override max epochs
.venv/bin/python train_torch.py --lr 0.0005 # override learning rate
.venv/bin/python train_torch.py --load model.safetensors # resume from weights
```
- Drop-in replacement for `./autokem train` — reads the same sheets, produces the same safetensors format
- The exported `autokem.safetensors` is directly loadable by the C inference code (`./autokem apply`)
- Requires: `pip install torch numpy` (venv at `.venv/`)
## Architecture
### Neural network
```
Input: 15x20x1 binary (300 values, alpha >= 0x80 → 1.0)
Conv2D(1→12, 3x3, same) → LeakyReLU(0.01)
Conv2D(12→16, 3x3, same) → LeakyReLU(0.01)
Flatten → 4800
Dense(4800→24) → LeakyReLU(0.01)
├── Dense(24→10) → sigmoid (shape bits A-H, J, K)
├── Dense(24→1) → sigmoid (Y-type)
└── Dense(24→1) → sigmoid (lowheight)
Total: ~117,388 params (~460 KB float32)
Conv2D(1→32, 7x7, pad=1) → SiLU
Conv2D(32→64, 7x7, pad=1) → SiLU
Global Average Pool → [batch, 64]
Dense(64→256) → SiLU
Dense(256→12) → sigmoid (10 shape bits + 1 ytype + 1 lowheight)
Total: ~121,740 params (~476 KB float32)
```
Training: Adam (lr=0.001, beta1=0.9, beta2=0.999), BCE loss, batch size 32, early stopping patience 10.
@@ -49,8 +61,9 @@ Training: Adam (lr=0.001, beta1=0.9, beta2=0.999), BCE loss, batch size 32, earl
|------|---------|
| `main.c` | CLI dispatch |
| `tga.h/tga.c` | TGA reader/writer — BGRA↔RGBA8888, row-order handling, per-pixel write-in-place |
| `nn.h/nn.c` | Tensor, Conv2D (same padding), Dense, LeakyReLU, sigmoid, Adam, He init |
| `safetensor.h/safetensor.c` | `.safetensors` serialisation — 12 named tensors + JSON metadata |
| `nn.h/nn.c` | Tensor, Conv2D (configurable padding), Dense, SiLU, sigmoid, global avg pool, Adam, He init |
| `safetensor.h/safetensor.c` | `.safetensors` serialisation — 8 named tensors + JSON metadata |
| `train_torch.py` | PyTorch training script — same data pipeline and architecture, exports C-compatible safetensors |
| `train.h/train.c` | Data collection from sheets, training loop, validation, label distribution |
| `apply.h/apply.c` | Backup, eligibility checks, inference, pixel composition |