nerdron 0.3.1

A simple and flexible neural network package

Nerdron

This is a Python package that provides a simple and flexible neural network implementation, with support for customizable layer configurations, activation functions, and training parameters.

Features

• Multi-layer Neural Network: The NeuralNetwork class allows you to create neural networks with an arbitrary number of layers.
• Customizable Activation Functions: The Activation class provides common activation functions like sigmoid, tanh, ReLU, and softmax, which can be used in the neural network layers.
• Backpropagation-based Training: The NeuralNetwork class implements the backpropagation algorithm for training the neural network on input-output pairs.
• User-friendly API: The package provides a simple and intuitive API, making it easy to integrate into your projects.

Installation

To install the neural network package, you can use pip:

pip install nerdron==0.3.1

Usage

Creating a Neural Network

To create a neural network, you can use the NeuralNetwork class. The constructor takes the following arguments:

• layers: A tuple of integers representing the number of neurons in each layer, including the input and output layers.
• activation: (optional) A callable that represents the activation function to be used in the layers. Defaults to Activation.SIGMOID.
• learning_rate: (optional) The learning rate to be used during training. Defaults to 0.01.
• epochs: (optional) The number of training epochs. Defaults to 1000.

Example:

from nerdron import NeuralNetwork, Activation

# Create a neural network with 2 input neurons, 2 hidden neurons, and 1 output neuron
nn = NeuralNetwork(layers=(2, 2, 1), activation=Activation.SIGMOID, learning_rate=0.1, epochs=1000)

Training the Neural Network

To train the neural network, you can use the train method, which takes the input data X and the target output y as arguments.

Example:

# Example data
X = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])
y = np.array([0, 1, 1, 1])

# Train the neural network
nn.train(X, y)

Making Predictions

After training the neural network, you can use the forward method to make predictions on new input data.

Example:

# Make predictions
print(nn.forward(np.array([0, 1])))  # Output: ~0.9
print(nn.forward(np.array([1, 0])))  # Output: ~0.9
print(nn.forward(np.array([0, 0])))  # Output: ~0.1
print(nn.forward(np.array([1, 1])))  # Output: ~0.9

Modules

activation.py

This module defines the Activation class, which provides common activation functions like sigmoid, tanh, ReLU, and softmax.

layer.py

This module defines the Layer class, which represents a single layer in the neural network. Each layer has an activation function, weights, and biases.

network.py

This module defines the NeuralNetwork class, which represents the entire neural network. It handles the forward propagation, backpropagation, and updating of the weights and biases.

Contribution

If you find any issues or have suggestions for improvements, feel free to open an issue or submit a pull request on the GitHub repository.

[!NOTE]
Under Development.