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Jensen-holm
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Parent(s):
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changing name to Numpy-Neuron
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README.md
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title:
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license: mit
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title: Numpy-Neuron
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emoji: π
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license: mit
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---
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## What is this? <br>
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The Numpy-Neuron is a GUI built around a neural network framework that I have built from scratch
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in [numpy](https://numpy.org/). In this GUI, you can test different hyper parameters that will be fed to this framework and used
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to train a neural network on the [MNIST](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) dataset of 8x8 pixel images.
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## β οΈ PLEASE READ β οΈ
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This application is impossibly slow on the HuggingFace CPU instance that it is running on. It is advised to clone the
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repository and run it locally.
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In order to get a decent classification score on the validation set of the MNIST data (hard coded to 20%), you will have to
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do somewhere between 15,000 epochs and 50,000 epochs with a learning rate around 0.001, and a hidden layer size
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over 10. (roughly the example that I have provided). Running this many epochs with a hidden layer of that size
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is pretty expensive on 2 cpu cores that this space has. So if you are actually curious, you might want to clone
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this and run it locally because it will be much much faster.
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`git clone https://huggingface.co/spaces/Jensen-holm/Numpy-Neuron`
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After cloning, you will have to install the dependencies from requirements.txt into your environment. (venv reccommended)
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`pip3 install -r requirements.txt`
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Then, you can run the application on local host with the following command.
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`python3 app.py`
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## Development
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In order to push from this GitHub repo to the hugging face space:
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`git push --force space main`
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app.py
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if __name__ == "__main__":
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with gr.Blocks() as interface:
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gr.Markdown("#
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gr.Markdown(
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"""
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## What is this? <br>
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is pretty expensive on 2 cpu cores that this space has. So if you are actually curious, you might want to clone
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this and run it locally because it will be much much faster.
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`git clone https://huggingface.co/spaces/Jensen-holm/
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After cloning, you will have to install the dependencies from requirements.txt into your environment. (venv reccommended)
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if __name__ == "__main__":
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with gr.Blocks() as interface:
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gr.Markdown("# Numpy Neuron")
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gr.Markdown(
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"""
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## What is this? <br>
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is pretty expensive on 2 cpu cores that this space has. So if you are actually curious, you might want to clone
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this and run it locally because it will be much much faster.
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`git clone https://huggingface.co/spaces/Jensen-holm/Numpy-Neuron`
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After cloning, you will have to install the dependencies from requirements.txt into your environment. (venv reccommended)
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