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license: mit |
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tags: |
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- automl |
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- genetic-programming |
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- loss-functions |
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- neural-networks |
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- bittensor |
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datasets: |
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- synapz/automl-genes |
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# AutoML Evolved Loss Functions |
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This repository contains evolved neural network loss functions discovered through distributed genetic programming on the Bittensor network (subnet 49 Hivetrain AutoML). |
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## Overview |
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The genes stored here represent novel loss functions optimized for: |
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- Image classification tasks |
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- Neural network training efficiency |
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- Improved convergence rates |
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## Repository Structure |
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/automl-genes ├── losses/ # Evolved loss function implementations ├── metrics/ # Performance metrics and evaluations └── metadata/ # Gene genealogy and evolution data |
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## Technical Details |
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Loss functions are evolved using: |
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- Genetic programming with population size 100 |
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- Tournament selection (size 3) |
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- Multi-architecture validation across: |
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- MLP |
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- ResNet |
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- MobileNet V3 |
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- EfficientNet V2 |
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## Usage |
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The evolved loss functions can be imported and used as drop-in replacements for standard PyTorch loss functions in deep learning projects. |
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## Project |
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Part of the Hivetrain AutoML subnet focused on discovering improved neural network components through distributed evolution. |
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For more information, visit: |
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- [DistributedAutoML Repository](https://github.com/Hivetensor/DistributedAutoML) |
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- [Hivetrain Discord](https://discord.gg/JpRSqTBBZU) |
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