Research on Optimization of Deep Learning in Handwritten Digit Recognition
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Aiming at the problem of balancing model accuracy and generalization ability in handwritten digit recognition tasks, this study takes the MNIST dataset as the research object, systematically compares the recognition performance of Multilayer Perceptrons (MLP) and Lightweight Convolutional Neural Networks (CNN). It optimizes model structures by adjusting the number of network layers, neurons, and convolution kernels, introduces Dropout regularization to suppress overfitting, and analyzes the impact of hyperparameters such as learning rate and batch size on model performance. Experimental results show that the lightweight CNN, relying on its advantage in spatial feature extraction, achieves a basic model recognition accuracy of 97.2%, significantly outperforming MLP's 95.8%. After structural optimization and Dropout regularization, the test accuracy of the lightweight CNN is improved to 98.6%, and overfitting is effectively alleviated. Among hyperparameters, the learning rate has the most significant impact on model convergence speed; when the optimal learning rate is 0.001, the model can quickly reach stable accuracy. This research provides an efficient lightweight model solution for handwritten digit recognition tasks, which is of reference value for image recognition applications in low-resource scenarios.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 it