Principle:LaurentMazare Tch rs Batch Evaluation
| Knowledge Sources | |
|---|---|
| Domains | Deep_Learning, Model_Evaluation |
| Last Updated | 2026-02-08 14:00 GMT |
Overview
Evaluation technique that computes model accuracy over a large test set by processing it in smaller batches with gradient tracking disabled.
Description
Batch evaluation splits a large test dataset into mini-batches, runs each through the model in inference mode (no gradients, no dropout), and aggregates the results to compute overall accuracy. This is necessary because the full test set may not fit in GPU memory at once. The method uses argmax comparison between predicted logits and ground truth labels, accumulating correct predictions across all batches to produce a final accuracy percentage.
Usage
Use at the end of each training epoch or after training completes to measure model performance on held-out test data. Always run with gradient tracking disabled for memory efficiency.
Theoretical Basis
Batch Evaluation Algorithm:
total_correct = 0
total_samples = 0
no_grad_guard() // Disable gradient tracking
For each batch (xs, ys) of test data:
logits = model.forward_t(xs, train=false)
predictions = argmax(logits, dim=-1)
correct = sum(predictions == ys)
total_correct += correct
total_samples += batch_size
accuracy = total_correct / total_samples