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ONNX Inference Contract - Affine MatMul plus Add Input

The named float32 input tensor payload for the Affine MatMul plus Add model, with explicit shapes and JSON values for runner-independent test setup.

Model inference testing Model evaluation JSON parsing

Preview — first 25 linesjson

{
  "format": "Novus ONNX tensor fixture v1",
  "tensors": {
    "X": {
      "dtype": "float32",
      "shape": [
        2,
        3
      ],
      "values": [
        [
          1.0,
          2.0,
          3.0
        ],
        [
          -1.0,
          0.5,
          2.0
        ]
      ]
    }
  }
}

Specifications

Contract: affine-matmul-add
Tensors: 1
Dtype: float32
Role: model input

Testing contract

Reference control

Scenario: Deserialize the named tensors and bind them to the Affine MatMul plus Add ONNX graph without implicit reshaping or dtype coercion.
Expected result: Bind 1 float32 tensor(s): X; inference then satisfies the group's expected-output contract.

What is a .json file?

JSON (JavaScript Object Notation) is a lightweight, text-based data-interchange format representing objects, arrays, strings, numbers, booleans, and null. It is language-independent, human-readable, and the dominant format for web APIs and configuration. It requires a single well-formed root value.

How to use this file

Use an example JSON file to test parsers and serializers, schema validation, Unicode and number-precision handling, and API request or response processing.

How to use this file for testing

“ONNX Inference Contract - Affine MatMul plus Add Input” is a deterministic Novus Examples fixture for Model inference testing, Model evaluation, JSON parsing. Small, validated ONNX graphs paired with named JSON inputs and expected tensor outputs. Use them to test runtime loading, dtype and shape handling, broadcasting, dynamic batches, reduction behavior, and numeric tolerances without downloading a production-sized model.

Documented properties for this file: model input. Compare results against paired or grouped companions on this page when present (clean↔damaged, searchable↔scanned, or format twins) so scores stay reproducible across runs.

Download the file once, keep the path stable in CI or local scripts, and treat the spec table as the contract: dimensions, seeds, field lists, and roles are intentional. Corrupt or invalid samples are labelled as such — expect parsers to fail loudly rather than silently accept them.

AI/ML fixtures are fully synthetic with documented schemas — no real people or data. Test data loaders, tokenizers, annotation converters, embedding/vector stores, or eval-metric parsers against the known structure and fixed seeds.

Code examples

import json

with open("affine-matmul-add-input.json") as f:
    data = json.load(f)
print(type(data), len(data))

Generated by generation/p6_core.py. Free for any use, no attribution required — license.