ncxlib is a lightweight and easy-to-use neural network library built in Python. It provides a simple API for constructing and training neural networks, along with tools for data preprocessing and generation.
- Modular Design: Easily build custom neural networks by combining different layers, activation functions, and loss functions.
- Data Handling: Includes data loaders for CSV and image data, with preprocessing capabilities like scaling and grayscaling.
- Training and Evaluation: Train your networks with various optimization algorithms and evaluate their performance.
- Extensible: Add your own custom layers, activations, and loss functions to expand the library's functionality.
pip install ncxlibHere's a quick example of how to use ncxlib to create and train a simple neural network:
# External imporst
import numpy as np
# Util imports
from ncxlib import generators, dataloaders
from ncxlib.util import train_test_split
# Neural network imports
from ncxlib.neuralnetwork import optimizers, losses
from ncxlib.preprocessing import MinMaxScaler
from ncxlib.neuralnetwork import NeuralNetwork, FullyConnectedLayer
from ncxlib.neuralnetwork import activations
from ncxlib.neuralnetwork.initializers import HeNormal, Zero
# ------- Generate some data using generators -------
generators.generate_training_data(to_csv=True)
# ------- Load data from generated csv and split it into train and test -------
loader = dataloaders.CSVDataLoader("training_data.csv")
X, y = loader.get_data()
X_train, X_test, y_train, y_test = train_test_split(X, y)
# ------- Configure model layers -------
model = NeuralNetwork([
FullyConnectedLayer(
n_neurons=3,
activation=activations.ReLU,
optimizer=optimizers.Adam(beta_1=0.9, beta_2=0.999, epsilon=1e-07),
name="first_hidden",
weights_initializer=HeNormal(),
bias_initializer=Zero()
),
FullyConnectedLayer(
n_neurons=5,
activation=activations.ReLU,
optimizer=optimizers.SGDMomentum(momentum = 0.9),
name="second_hidden",
initializer=HeNormal(),
),
FullyConnectedLayer(
n_neurons=2,
activation=activations.Sigmoid,
optimizer=optimizers.RMSProp(decay_rate = 0.8)
)
],
loss_fn=losses.BinaryCrossEntropy
)
# ------- Train model and evaluate accuracy -------
model.train(X_train, y_train, epochs=20, learning_rate=0.01)
model.evaluate(X_test, y_test)This project is licensed under the MIT License