TFCheck : A TensorFlow Library for Detecting Training Issues in Neural\n Network Programs
Bibliographic record
Abstract
The increasing inclusion of Machine Learning (ML) models in safety critical\nsystems like autonomous cars have led to the development of multiple\nmodel-based ML testing techniques. One common denominator of these testing\ntechniques is their assumption that training programs are adequate and\nbug-free. These techniques only focus on assessing the performance of the\nconstructed model using manually labeled data or automatically generated data.\nHowever, their assumptions about the training program are not always true as\ntraining programs can contain inconsistencies and bugs. In this paper, we\nexamine training issues in ML programs and propose a catalog of verification\nroutines that can be used to detect the identified issues, automatically. We\nimplemented the routines in a Tensorflow-based library named TFCheck. Using\nTFCheck, practitioners can detect the aforementioned issues automatically. To\nassess the effectiveness of TFCheck, we conducted a case study with real-world,\nmutants, and synthetic training programs. Results show that TFCheck can\nsuccessfully detect training issues in ML code implementations.\n
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".