# Model Layer Name Validation

> Use when when deploying a TensorFlow Serving instance for the NP Classifier or any model-agnostic service where client code hardcodes layer names; before running inference pipelines; after model updates or configuration changes to catch layer name drift early.

- Skill: `holobiomicslab/model-layer-name-validation` (Agent Skill)
- Install (CLI): `npx skillmds@latest add holobiomicslab/model-layer-name-validation`
- Raw SKILL.md: https://api.skillmd.com/api/skills/holobiomicslab/model-layer-name-validation/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- License: CC-BY-4.0
- Author: HolobiomicsLab (https://skillmd.com/u/holobiomicslab)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/holobiomicslab/model-layer-name-validation

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# model-layer-name-validation

## Summary

Validates that a TensorFlow Serving model exposes the correct input and output layer names by querying the /model/metadata endpoint and comparing against expected specifications. This ensures downstream code that depends on specific layer names will function correctly.

## When to use

When deploying a TensorFlow Serving instance for the NP Classifier or any model-agnostic service where client code hardcodes layer names; before running inference pipelines; after model updates or configuration changes to catch layer name drift early.

## When NOT to use

- Model is served via a non-TensorFlow Serving endpoint (e.g., TorchServe, KServe, or direct REST API without /model/metadata); the metadata endpoint is not exposed or is disabled.
- Layer names are dynamically generated or versioned at runtime and are not expected to match static specifications.

## Inputs

- TensorFlow Serving instance URL (host:port)
- Model name string
- Expected input layer name specification (array of strings)
- Expected output layer name specification (string)

## Outputs

- Verification report (JSON or text) documenting metadata endpoint response
- Layer name validation outcome (pass/fail)
- Extracted input layer names (array)
- Extracted output layer name (string)

## How to apply

Start the TensorFlow Serving container (typically via docker-compose), then send a GET request to the /model/metadata endpoint (e.g., http://localhost:8501/v1/models/<model_name>/metadata). Parse the JSON response to extract the input layer names and output layer name fields. Compare the extracted names against the expected specification: for NP Classifier, input layers must be exactly 'input_2048' and 'input_4096', and output layer must be exactly 'output'. If any mismatch is found, flag it and halt downstream inference; otherwise, document the metadata query result and validation outcome in a verification report.

## Related tools

- **TensorFlow Serving** (Exposes the /model/metadata endpoint and serves the model whose layer names are being validated)
- **docker-compose** (Orchestrates the TensorFlow Serving container deployment locally)
- **Python** (Used to parse JSON metadata response and perform layer name comparison)

## Examples

```
import requests; resp = requests.get('http://localhost:8501/v1/models/np_classifier/metadata'); metadata = resp.json(); inputs = metadata['model_spec'][0]['signature_def']['serving_default']['inputs']; outputs = metadata['model_spec'][0]['signature_def']['serving_default']['outputs']; assert list(inputs.keys()) == ['input_2048', 'input_4096']; assert list(outputs.keys()) == ['output']; print('Validation passed')
```

## Evaluation signals

- HTTP response status from /model/metadata endpoint is 200 and contains valid JSON
- Extracted input layer names array exactly matches expected specification (e.g., ['input_2048', 'input_4096'])
- Extracted output layer name string exactly matches expected specification (e.g., 'output')
- Verification report is generated and filed with timestamp and full metadata query result
- No downstream code failures due to layer name mismatches during inference

## Limitations

- Validation is a point-in-time check; layer names could change after this skill is applied without re-running validation
- The /model/metadata endpoint must be accessible and enabled in TensorFlow Serving configuration; some deployments may disable it for security reasons
- Does not validate the *structure* or *shape* of layers, only their names—inference may still fail due to incompatible tensor dimensions or data types

## Evidence

- [other] research_question: "What are the correct input and output layer names that must be validated when querying the /model/metadata endpoint exposed through TensorFlow Serving for the NP Classifier models?"
- [other] endpoint_and_layer_spec: "The /model/metadata endpoint exposes input layer names 'input_2048' and 'input_4096' and output layer name 'output'."
- [other] workflow_procedure: "Send a GET request to the TensorFlow Serving /model/metadata endpoint (typically http://localhost:8501/v1/models/<model_name>/metadata). 3. Parse the JSON response to extract input layer names and"
- [other] validation_check: "Validate that the input layers are exactly 'input_2048' and 'input_4096' and output layer is exactly 'output'."
- [readme] readme_layer_names: "Input layers' names should be "input_2048" and "input_4096"

Output layer's name should be "output""
- [readme] readme_metadata_change: "If the model input names change, then we need to change it in the code"