Analysis on the Selection of the Appropriate Batch Size in CNN Neural Network
Why this work is in the frame
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Bibliographic record
Abstract
Batch Size is an essential hyper-parameter in deep learning. Different chosen batch sizes may lead to various testing and training accuracies and different runtimes. Choosing an optimal batch size is crucial when training a neural network. The scientific purpose of this paper is to find an appropriate range of batch size people can use in a convolutional neural network. The study is conducted by changing the hyper-parameter batch size and observing the influences when training some commonly used convolutional neural networks (Mnist, Fashion Mnist and CIFAR-10). The experiment results suggest it is more likely to obtain the most accurate model when choosing the mini-batch size between 16 and 64. In addition, the experiments discuss the effect of different sizes of datasets, neural network depth, and whether the batch size is a power of 2 on the conclusions. Therefore, when training a CNN model, people could first choose a batch size of 32 and decrease it for accuracy or increase it for efficiency.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it