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name: data-and-utilities description: "Routes TensorLayer file, preprocessing, iteration, and visualization workflows." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Data and Utilities

Use this sub-skill for TensorLayer file helpers, preprocessing, minibatch iteration, TFRecord round-trips, and lightweight visualization workflows. This is the route for data handling before or around model training.

Typical requests

• Load a dataset or inspect a packaged dataset helper.
• Apply affine/image preprocessing to a small fixture.
• Build or read a TFRecord file.
• Iterate over minibatches or sequence batches.
• Save or display a small image with TensorLayer helpers.

Read first

• references/api-reference.md for the data-helper surface and verified signatures.
• references/workflows.md for tiny preprocessing and TFRecord patterns.
• references/troubleshooting.md for download, path, OpenCV, and schema failures.

Bundled checks

• scripts/smoke_prepro.py checks affine rotation and transform behavior on a tiny synthetic image.
• scripts/smoke_tfrecord.py writes and reads a tiny TFRecord in a temporary directory.

Boundaries

Include here:

• tensorlayer.files
• tensorlayer.prepro
• tensorlayer.iterate
• tensorlayer.visualize
• tiny dataset-loading and preprocessing helpers

Exclude or route elsewhere:

• core layer/model definitions -> core-modeling
• supervised training loops and CLI help -> training-and-cli
• pretrained image model constructors and app wrappers -> vision-and-apps
• NLP and seq2seq workflows -> text-and-sequence
• RL reward utilities and episode helpers -> reinforcement-learning

Fast path

1. Identify whether the problem is file, preprocessing, iteration, or visualization.
2. Prefer a synthetic or tiny fixture over bundled large data.
3. Use the smoke scripts before moving to the full repo example.