Comparison of Harris Performance as Activation Function to Rectified Linear Unit (ReLU), Leaky ReLU, and Tanh in Convolutional Neural Network for Image Classification
Bibliographic record
Abstract
Activation functions (AFs) are the building blocks of a deep neural network (DNN) to perform image classification effectively by handling nonlinear data and extracting complex features and patterns. This paper introduces a new activation function (AF) called “Harris” a piecewise and nonlinear nonmonotonic AF inspired from the field of photonics. The AF was integrated to a simple convolutional neural network (CNN) using Canadian Institute for Advanced Research (CIFAR-10) dataset to determine the model’s performance in terms of accuracy in training and testing, image classification capability, and feature extraction. Harris was able to exceed the leaky Rectified Linear Unit (ReLU) and hyperbolic tangent function (tanh) target accuracies from α-values −0.80 to −1.00 in image classification, while the testing accuracies were able to exceed the target accuracies of ReLU from α-values −0.80 to −0.95. It was able to handle negative values solving the dead neuron problem and extract complex features through its feature maps which improve the F1-scores of the CNN model in image classification.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".