JavaNet
Understanding and Implementing Neural Networks in Java from Scratch 💻 Learning the popular concept in the most 💪strongly typed language
Install / Use
/learn @SuyashSonawane/JavaNetREADME
About The Project
A simple Neural Network class completely written in Java from scratch without using any external libraries.
A Neural Network is a type of computational system which represents the human brain in a smaller manner. The Neurons are connected with Synapses, these concepts were taken from the 🧠. The system simulates the learning process of the brain by adjusting the Weights of the synapses by the process of Backpropagation.
Here's why you should consider using it:
- Easy to use and test
- Logging features
- Customizable
I also developed a even more feature rich JavaScript library which can be found here Lite Neural Network
Getting Started
- Place
NeuralNetwork.javain your project directory - Instantiate the neuralnetwork object
- Process the data in proper format
- Train the model
- Test the model
For full explanation, please refer to the Article
<!-- USAGE EXAMPLES -->Usage
Instantiating the neuralnetwork object
// In the sample we are creating a neural network with 2 input features and 1 output with 10 hidden nodes
// default constructor
NeuralNetwork nn = new NeuralNetwork(2, 10, 1);
// constructor with custom learning rate
NeuralNetwork nn_custom_lr = new NeuralNetwork(2, 10, 1, 0.01);
// constructor with multi-threading set to true
NeuralNetwork nn_with_multithreading = new NeuralNetwork(2, 10, 1, true);
// constructor with multi-threading set to true and custom learning rate
NeuralNetwork nn_custom_lr_with_multithreading = new NeuralNetwork(2, 10, 1, 0.01, true);
Data Format
// As we have 2 input features we created the 2d double array for X and 1d double array for Y
double[][] X = { { 0, 0 }, { 1, 0 }, { 0, 1 }, { 1, 1 } };
double[][] Y = { { 0 }, { 1 }, { 1 }, { 0 } };
Training the model
// Here we pass the data and specify the number of epochs
nn.fit(X, Y, 500); // silent learn
nn.fit(X,Y,500,0); // logging set to 0, shows training time and average error
nn.fit(X,Y,500,1); // logging set to 1, shows logs for each epoch
Testing the model
// Here we used 4 data-points to test the model, we iterate and pass each data-point to the model
List<Double> output;
double[][] input = { { 0, 0 }, { 0, 1 }, { 1, 0 }, { 1, 1 } };
for (double d[] : input) {
output = nn.predict(d);
System.out.println(output.toString());
}
Roadmap
See the open issues for a list of proposed features (and known issues).
<!-- CONTRIBUTING -->Contributing
Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/AmazingFeature) - Commit your Changes (
git commit -m 'Add some AmazingFeature') - Push to the Branch (
git push origin feature/AmazingFeature) - Open a Pull Request
Project status
- [x] Basic Library
- [x] Documentation
- [x] Explanation Article
- [x] Multi-Threading Support
- [ ] Interface to load data easily
- [ ] Multiple layer architecture
- [ ] Improve performance
- [ ] Unit Testing
- [ ] Production
License
Distributed under the MIT License. See LICENSE for more information.
Contact
Your Name - @suyashysonawane - suyashsonawane007@gmail.com
Project Link: https://github.com/suyashsonawane/JavaNet
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