Principle:Ggml org Ggml Model Evaluation
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
FORWARDbuild 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.
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