How to develop your network with Python and Keras
Bibliographic record
Abstract
Chapter Contents: 7.1 Introduction 7.1.1 TensorFlow and Keras 7.1.2 Tensors 7.2 Google Colaboratory 7.2.1 How to work with GPU? 7.2.2 How to connect Google Drive? 7.3 Examples 7.3.1 Noise filtering 7.3.1.1 Step 1 — Define a generator for the dataset 7.3.1.2 Step 2 — Build a model 7.3.1.3 Step 3 — Train and test the model 7.3.2 Classification 7.3.2.1 Step 1 — Define a generator for the dataset 7.3.2.2 Step 2 — Build a model 7.3.2.3 Step 3 — Train and test the model 7.3.3 Detection 7.3.3.1 Step 1 — Define a generator for the dataset 7.3.3.2 Step 2 — Build a model 7.3.3.3 Step 3 — Train and test the model 7.4 Conclusions References
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.250 | 0.288 |
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 source (direct Gemma or distilled Codex), 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".