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@@ -1,13 +1,56 @@ +# multilayer-perceptron + +A simple multilayer perceptron implementation in NumPy. + +## Requirements + +- Python >=3.10 (pinned to 3.14 via `.python-version`) +- [uv](https://docs.astral.sh/uv/) + +## Getting started + +```bash +# install dependencies (creates .venv, respects uv.lock) +uv sync --all-groups + +# quick sanity check (no data files needed) +uv run python -c "from MultiLayerPerceptron.main import MultiLayerPerceptron; import numpy as np; mlp = MultiLayerPerceptron([2,4,1]); print(mlp.frontprop(np.array([0,1])) )" + +# download MNIST and prepare files (one-time, ~60k train / 10k test) +uv run mlp-download +# creates data/train.npy, data/test.npy, mnist.npy (initial weights for [784,32,16,10]) + +# run the MNIST demo (requires data/train.npy, data/test.npy, mnist.npy) +uv run python -m MultiLayerPerceptron.main +``` + +Manual data tooling (MNIST `idx` → `npy` via `idx2numpy`): + +```bash +uv run python -m MultiLayerPerceptron.download --help +uv run python -m MultiLayerPerceptron.data # legacy, expects idx files in cwd +``` + +Development: + +```bash +uv run ruff check . +uv run ruff format . +uv run ty check +uv run pytest +``` + ## How to use -1. Download data and save them in `.npy` format. See `data.py` for details. -2. Write a run file for processing data. See script in `main.py` for details. +1. Download data and save them in `.npy` format. See `MultiLayerPerceptron/data.py` for details. +2. Write a run file for processing data. See script in `MultiLayerPerceptron/main.py` for details. ## TODO -* [ ] Print stages -* [ ] Learning samples method -* [ ] Save mlp -* [ ] Choose error -* [ ] Choose function -* [ ] CLI -* [ ] Multithread / Multiprocess
\ No newline at end of file + +- [ ] Print stages +- [ ] Learning samples method +- [ ] Save mlp +- [ ] Choose error +- [ ] Choose function +- [ ] CLI +- [ ] Multithread / Multiprocess |