lars
a4c0443e01
Add bigger WGAN config (hidden_dim=512, n_blocks=6)
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Scaled-up variant of the already-trained wgan_h128_b4_physical.toml
benchmark config, same training recipe (mode/epochs/lr/warmup),
increased model capacity.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com >
2026-07-30 17:08:46 +02:00
lars
af2ee7c7ce
Add gumbel router configs sweeping learnable-knob combinations
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Extends the 10-expert EnergyRouter + physical-conditioning benchmark
config with the new opt-in gumbel combine weights, isolating the
learn_centers/learn_temperature axis: none, centers only, and
centers+temperature.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com >
2026-07-30 16:40:02 +02:00
lars
9277d79dff
Implement Phase 1: full data pipeline, model, training, and config support
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- 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 >
2026-06-17 10:48:03 +02:00
lars