21 lines
1.3 KiB
Markdown
21 lines
1.3 KiB
Markdown
# mnist
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This is a neural network trained to predict images of the [MNIST database](https://en.wikipedia.org/wiki/MNIST_database).
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It's written in python without making use of ML libraries directly, using [Apple's MLX](https://github.com/ml-explore/mlx) to accelerate matrix computations with the GPU.
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This makes the scripts very fast on Apple Silicon machines since MLX makes great use of the SoC shared memory and lazy evaluation for efficiency.
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The code is commented in detail and quite simple to understand with basic understanding of neural networks.
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## Implementation
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The network is structured to use 4 hidden layers of size 256x256. For activation of hidden layers [ReLU](https://en.wikipedia.org/wiki/Rectified_linear_unit) is used
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and loss is calculated with [cross-entropy](https://en.wikipedia.org/wiki/Cross-entropy). For optimization Adam is used (check the references section).
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## Running
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To run the scripts you first need to download the dataset and the dependencies with
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```sh
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./data.sh
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pip install -r requirements.txt
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```
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After that you can train the model by running the `train.py` script. To test the model you can run `test.py` instead. Accuracy should be around 97%.
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## References
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- [Adam optimization algorithm](https://arxiv.org/abs/1412.6980)
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- [Google ML crash course](https://developers.google.com/machine-learning/crash-course)
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