lars
9277d79dff
- Data pipeline: loader (parquet→numpy), transforms (log, local-frame Rodrigues rotation, Normalizer), StepsDataset with event-ID-based split - Model: SinusoidalEmbedding, ConditionEncoder, ResBlock, DenoisingMLP - Schedule: cosine DDPM and conditional flow matching loss (Lipman 2022) - Samplers: flow (Euler ODE), DDPM ancestral, DDIM deterministic - Training loop: AdamW + cosine LR, grad clipping, best-val checkpoint - Validation: per-dimension marginal summary (normalised space) - CLI: TOML config support with CLI-overrides; hyperparam-encoded output directory; config.toml with git hash saved into each run's checkpoint dir - 21 unit tests covering transforms, network, flow/DDPM losses, dataset splits Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
15 lines
231 B
TOML
15 lines
231 B
TOML
# git: c3bf3abebfe29a10fe42b9cbafbb3460ab78d243
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[train]
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mode = "flow"
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epochs = 100
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batch_size = 4096
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lr = 3e-4
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val_fraction = 0.1
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num_workers = 4
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[model]
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hidden_dim = 256
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n_blocks = 6
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emb_dim = 16
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