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---
title: Numpy-Neuron
emoji: 🔙
colorFrom: yellow
colorTo: blue
sdk: gradio
sdk_version: 4.26.0
app_file: gradio_app.py
pinned: false
license: mit
---
# Numpy-Neuron
A small, simple neural network framework built using only [numpy](https://numpy.org) and python (duh). Check it out on [PyPI](https://pypi.org/project/numpyneuron/)
## Install
`pip install numpyneuron`
## Example
```py
from sklearn import datasets
from sklearn.preprocessing import OneHotEncoder
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
import numpy as np
from numpyneuron import (
NN,
Relu,
Sigmoid,
CrossEntropyWithLogits,
)
RANDOM_SEED = 2
def preprocess_digits(
seed: int,
) -> tuple[np.ndarray, ...]:
digits = datasets.load_digits(as_frame=False)
n_samples = len(digits.images)
data = digits.images.reshape((n_samples, -1))
y = OneHotEncoder().fit_transform(digits.target.reshape(-1, 1)).toarray()
X_train, X_test, y_train, y_test = train_test_split(
data,
y,
test_size=0.2,
random_state=seed,
)
return X_train, X_test, y_train, y_test
def train_nn_classifier(
X_train: np.ndarray,
y_train: np.ndarray,
) -> NN:
nn_classifier = NN(
epochs=2_000,
hidden_size=16,
batch_size=1,
learning_rate=0.01,
loss_fn=CrossEntropyWithLogits(),
hidden_activation_fn=Relu(),
output_activation_fn=Sigmoid(),
input_size=64, # 8x8 pixel grid images
output_size=10, # digits 0-9
seed=2,
)
nn_classifier.train(
X_train=X_train,
y_train=y_train,
)
return nn_classifier
if __name__ == "__main__":
X_train, X_test, y_train, y_test = preprocess_digits(seed=RANDOM_SEED)
classifier = train_nn_classifier(X_train, y_train)
pred = classifier.predict(X_test)
pred = np.argmax(pred, axis=1)
y_test = np.argmax(y_test, axis=1)
accuracy = accuracy_score(y_true=y_test, y_pred=pred)
print(f"accuracy on validation set: {accuracy:.4f}")
```
## Running Example
1. `git clone https://Jensen-holm/Numpy-Neuron.git && cd Numpy-Neuron`
2. `virtualenv venv` (can use other tools to create virtual environment)
3. `source venv/bin/activate`
4. `pip install -r requirements.txt numpyneuron`
5. `python3 example.py`
## Roadmap
**Optimizers**
I would love to add the ability to modify the learning rate over each epoch to ensure
that the gradient descent algorithm does not get stuck in local minima as easily.
## Gradio app demo development notes
The remote added to this repo so that it runs on hugging face spaces
`git remote add space [email protected]:spaces/Jensen-holm/Numpy-Neuron`
The command to force push to that space
`git push --force space main`
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