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name: core-modeling description: "Routes TensorLayer layer, model, serialization, and pretrained-constructor workflows." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Core Modeling

Use this sub-skill for TensorLayer layer composition, custom model classes, pretrained constructors, and save/load behavior. This is the route for model-building questions that stay inside the core TensorLayer API surface.

Typical requests

• Build a static Input -> layer -> Model network.
• Write a custom Model subclass.
• Save or restore weights or model state.
• Compare static and dynamic TensorLayer model patterns.
• Instantiate pretrained vision constructors with pretrained=False.

Read first

• references/api-reference.md for verified signatures and supported model APIs.
• references/workflows.md for tiny model patterns and round-trip recipes.
• references/troubleshooting.md for import, naming, serialization, and constructor failures.

Bundled check

• scripts/smoke_model.py builds a tiny dense model, runs a forward pass, saves and reloads weights, and can optionally instantiate the main pretrained image constructors without downloading weights.

Boundaries

Include here:

• tensorlayer.layers
• tensorlayer.models
• tensorlayer.activation
• tensorlayer.cost
• tensorlayer.initializers
• tensorlayer.optimizers
• save/load and model-naming behavior

Exclude or route elsewhere:

• data loading, preprocessing, TFRecord, or visualization helpers -> data-and-utilities
• tl.utils.fit/test/predict, tl train, or distributed execution -> training-and-cli
• object detection, pose wrappers, and application tutorials -> vision-and-apps
• text, seq2seq, and NLP helpers -> text-and-sequence
• reward utilities and RL examples -> reinforcement-learning

Fast path

1. Build the smallest model that matches the question.
2. Keep pretrained=False unless the user explicitly needs weights.
3. Use the smoke script before expanding the architecture.