README.md

metadata

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 and python (duh). Check it out on PyPI

Install

pip install numpyneuron

Example

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