Basic Neural Network Library

The Bush School CPJava final project 2022

This is a very basic Java Neural Network library based on the one built by Daniel Shiffman in this playlist using the Efficient Java Matrix Library (EJML).

The library can also be used with Processing. Just download the jar-file (see below) and drag it into your sketch.

If you want to learn more about Neural Networks check out these YouTube-playlists:

• Neural Networks - The Nature of Code by The Coding Train (Daniel Shiffman)
• Neural Networks by 3Blue1Brown

Features

• Neural Network with variable amounts of inputs, hidden nodes and outputs
• Multiple hidden layers
• Activation functions: Sigmoid, Tanh, ReLu
• Adjustable learning rate
• Fully connected
• Support for genetic algorithms: copy-, mutate-, and merge-functionality
• Save the weights and biases of a NN to a JSON-file
• Generate a NeuralNetwork-object from a JSON-file

Getting Started

Use the library

Constructors:

// Neural network with 2 inputs, 1 hidden layer, 4 hidden nodes and 1 output
NeuralNetwork nn0 = new NeuralNetwork(2, 4, 1);

// Neural network with 2 inputs, 2 hidden layers, 4 hidden nodes and 1 output
NeuralNetwork nn1 = new NeuralNetwork(2, 2, 4, 1);

Train and guess:

// Train the neural network with a training dataset (inputs and expected outputs)
nn.train(trainingDataInputs, trainingDataTargets);

// Guess for the given testing data is returned as an array (double[])
nn.guess(testingData);

Read and write from/to file:

// Reads from a (previously generated) JSON-file the nn-Data and returns a NeuralNetwork-object
NeuralNetwork myNN = NeuralNetwork.readFromFile();
// Load from a specifiy file (by default it will look for a file called "nn_data.json")
NeuralNetwork myNN = NeuralNetwork.readFromFile("my_nn_data.json");

// Writes a JSON-file with the current "state" (weights and biases) of the NN
myNN.writeToFile();
// Specify a custom file name (by default it will be saved as "nn_data.json")
myNN.writeToFile("my_nn_data");

Adjust the learning rate:

// Set the learning rate (Initially the learning rate is 0.1)
nn.setLearningRate(0.01);

// Get current learning rate
nn.getLearningRate();

Use different activation functions:

// Set the activation function (By default Sigmoid will be used)
nn.setLearningRate(ActivationFunction.TANH);

// Get name of currently used activation function
nn.getActivationFunctionName();

Use this library with genetic algorithms:

// Make an exact and "independent" copy of a Neural Network
NeuralNetwork nn2 = nn1.copy();

// Merge the weights and biases of two Neural Networks with a ratio of 50:50
NeuralNetwork merged = nnA.merge(nnB);

// Merge the weights and biases of two Neural Networks with a custom ratio (here: 20:80)
NeuralNetwork merged = nnA.merge(nnB, 0.2);

// Mutate the weights and biases of a Neural Network with custom probability
nn.mutate(0.1);

More detailed examples can be found below.

Download

If you want to use this library you can download v0.5 here.

Examples

• TBD

Libraries & Tools used in this project

• EJML used for Matrix math
• Google Gson library