Principle:Ggml org Ggml Model Evaluation

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Summary

Model Evaluation is the process of evaluating trained model performance on held-out test data. In GGML, this involves running a forward-only inference pass (no backward pass), computing metrics such as loss and accuracy, and optionally estimating uncertainty over those metrics.

Theory

Model evaluation performs forward-only inference on a dataset, meaning no gradient computation or backward pass is required. This makes evaluation faster and less memory-intensive than training. The key components are:

• Forward pass: The model graph is executed in FORWARD build mode, propagating inputs through the network to produce predictions.
• Metric computation: Accumulated predictions are compared against ground-truth labels to compute aggregate metrics such as loss and accuracy.
• Uncertainty estimation: Standard error can be computed over per-sample metrics to quantify confidence in the reported values.

Metrics

Metric	Description
Cross-entropy loss	Measures prediction confidence; lower values indicate the model assigns higher probability to the correct class.
Classification accuracy	Fraction of samples for which the predicted class matches the ground-truth label.
Per-sample predictions	The predicted class index for each individual sample in the evaluation dataset.

Key Properties

• Forward-only pass: Evaluation uses build_type=FORWARD, which skips the backward pass entirely. This is faster and uses less memory than training since no gradient computation is performed.
• Data split handling: Evaluation uses all data as eval (idata_split=0), unlike training which splits data into train and validation subsets.

Related

• Implementation:Ggml_org_Ggml_Mnist_model_eval