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name: training-experiments description: "Routes shared training-loop, metric, and notebook-experiment tasks for the Siamese-triplet repository." metadata: disco-role: operating disable-model-invocation: true license: BSD 3-Clause

Training Experiments

Use this sub-skill when the task is about the repository's shared fit loop, the sample metrics, the MNIST/FashionMNIST notebooks, or the tiny end-to-end smoke that proves the whole surface still works together.

Covered surface

• fit, train_epoch, and test_epoch
• AccumulatedAccuracyMetric
• AverageNonzeroTripletsMetric
• Notebook-style training recipes for MNIST and FashionMNIST
• Embedding extraction and 2D plotting helpers from the notebooks
• Tiny synthetic training passes that avoid dataset downloads

When to route here

Read this sub-skill for tasks that mention:

• training or validation loops
• optimizer or scheduler wiring
• notebook experiment reconstruction
• embedding visualization
• per-epoch logging
• metric objects for classification or online triplet learning

What to do first

1. Read references/workflows.md for the distilled notebook recipes.
2. Read references/troubleshooting.md if a scheduler, metric, or training loop behaves oddly.
3. Run scripts/tiny_training_smoke.py when you want a no-download end-to-end check.

Evidence signals

• Source evidence comes from trainer.py, metrics.py, README experiment descriptions, and both notebooks.
• The notebooks are expensive examples; the smoke script is the safe executable proxy.
• The fit-loop contract is intentionally broad, so diagnose loader, model, and loss arity together.

Short workflow

• Match the loader shape to the model and loss shape before calling fit.
• Use AccumulatedAccuracyMetric for classification setups.
• Use AverageNonzeroTripletsMetric when the online triplet loss returns a mined-triplet count.
• For notebook parity, keep the common defaults unless the task explicitly asks for a change:
  • 2D embeddings
  • margin = 1.0
  • Adam
  • StepLR(..., step_size=8, gamma=0.1)

Decision points

• Choose classification training when you want a baseline embedding plus class head.
• Choose siamese or triplet training when you want pairwise or triplet supervision.
• Choose online mining when batch size and hard-example selection matter more than random pair or triplet sampling.
• Use a synthetic tiny dataset for verification rather than downloading the full torchvision datasets.

Common failure modes

• The model outputs, loss inputs, and loader batches do not agree on arity.
• Metrics are stateful and must be reset between epochs or smoke passes.
• The repository's notebook code emits a modern PyTorch scheduler-order warning.
• A long notebook run is attempted when only a smoke check was needed.

Acceptance checks

• A one-epoch classification smoke runs with ClassificationNet, NLLLoss, and AccumulatedAccuracyMetric.
• An online triplet smoke runs with EmbeddingNet, OnlineTripletLoss, and AverageNonzeroTripletsMetric.
• Warnings from modern PyTorch are documented rather than treated as silent failures.
• No smoke check downloads MNIST or FashionMNIST.

Read next

• references/workflows.md for the notebook recipes and distilled defaults.
• references/troubleshooting.md for fit-loop and metric issues.
• ../embedding-losses-mining/SKILL.md if the training issue is actually a loss or selector issue.
• ../dataset-sampling/SKILL.md if the issue is a loader or sampler issue.