Spaces:
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title: Numpy-Neuron
emoji: 🔙
colorFrom: yellow
colorTo: blue
sdk: gradio
sdk_version: 4.26.0
app_file: app.py
pinned: false
license: mit
What is this?
The Numpy-Neuron is a GUI built around a neural network framework that I have built from scratch in numpy. In this GUI, you can test different hyper parameters that will be fed to this framework and used to train a neural network on the MNIST dataset of 8x8 pixel images.
⚠️ PLEASE READ ⚠️
This application is impossibly slow on the HuggingFace CPU instance that it is running on. It is advised to clone the repository and run it locally.
In order to get a decent classification score on the validation set of the MNIST data (hard coded to 20%), you will have to do somewhere between 15,000 epochs and 50,000 epochs with a learning rate around 0.001, and a hidden layer size over 10. (roughly the example that I have provided). Running this many epochs with a hidden layer of that size is pretty expensive on 2 cpu cores that this space has. So if you are actually curious, you might want to clone this and run it locally because it will be much much faster.
git clone https://huggingface.co/spaces/Jensen-holm/Numpy-Neuron
After cloning, you will have to install the dependencies from requirements.txt into your environment. (venv reccommended)
pip3 install -r requirements.txt
Then, you can run the application on local host with the following command.
python3 app.py
Development
In order to push from this GitHub repo to the hugging face space:
git push --force space main