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2.1 KiB
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{"root_cause":"The validation path is almost certainly not measuring the random model's outputs against the loader targets. A variable-aliasing, stale-tensor, or metric/loss wiring bug is likely causing target-derived values to be treated as predictions.","confidence":0.98,"evidence":["Observation: A 100-class random classifier should have cross-entropy near ln(100) = 4.605 and accuracy near 1%.","Observation: The reported loss is 0.04 and accuracy is 96% before any optimizer step.","Observation: A separately loaded raw sample only checks the underlying data, not the tensors and variables used inside the validation loop.","Inference: The joint deviation in both metrics is far too large to plausibly arise from random initialization.","Inference: Target leakage or prediction/target aliasing in evaluation is more probable than an unusual class distribution, because 0.04 cross-entropy requires high confidence on the reported target as well as high accuracy."],"competing_hypotheses":["The supposedly random model actually loaded trained weights.","The validation loader or preprocessing leaks labels into model inputs.","The validation set is overwhelmingly one class and initialization produces a strongly biased prediction, although this poorly explains cross-entropy of 0.04."],"first_action":"Take one exact batch from the validation loader, run `logits = model(images)` once, then independently compute `cross_entropy(logits, labels)` and `(logits.argmax(-1) == labels).float().mean()` outside the validation/metric code; also print tensor identities, shapes, label histogram, and several `(prediction, label, true-class probability)` rows.","prediction_if_true":"The independently recomputed metrics will be near loss 4.6 and accuracy 1%, while the existing validation path still reports about 0.04 and 96%, localizing the failure to evaluation wiring.","prediction_if_false":"The independent calculation will reproduce about 0.04 loss and 96% accuracy, shifting suspicion to loaded weights, label leakage in the loader tensors, or pathological validation data.","immediate_changes":[],"fallback_logic":[]} |