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Understanding and Implementing Neural Networks in Java from Scratch 💻 Learning the popular concept in the most 💪strongly typed language

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<a href="https://github.com/suyashsonawane/JavaNet">
 <img src="./docs/images/logo.png" alt="Logo" width="80" height="80">
 </a>

<h3 align="center">JavaNet</h3>

Understanding and Implementing Neural Networks in Java from Scratch 💻 Learning the popular concept in the most 💪strongly typed language
 
 <a href="https://github.com/suyashsonawane/JavaNet">Explore the docs »</a>
 
 
 <a href="https://github.com/SuyashSonawane/JavaNet/blob/main/Driver.java">View Demo</a>
 ·
 <a href="https://github.com/suyashsonawane/JavaNet/issues">Report Bug</a>
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 <a href="https://github.com/suyashsonawane/JavaNet/issues">Request Feature</a>

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## 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](https://suyashsonawane.me/Lite-Neural-Networks/)

## Getting Started
1. Place `NeuralNetwork.java` in your project directory
2. Instantiate the neuralnetwork object
3. Process the data in proper format
4. Train the model
5. Test the model

_For full explanation, please refer to the [Article](https://towardsdatascience.com/understanding-and-implementing-neural-networks-in-java-from-scratch-61421bb6352c)_

## Usage

### Instantiating the neuralnetwork object
---

```java
// 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
---
```java
// 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
---
```java
// 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
---
```java
// 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](https://github.com/suyashsonawane/JavaNet/issues) for a list of proposed features (and known issues).

## 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**.

1. Fork the Project
2. Create your Feature Branch (`git checkout -b feature/AmazingFeature`)
3. Commit your Changes (`git commit -m 'Add some AmazingFeature'`)
4. Push to the Branch (`git push origin feature/AmazingFeature`)
5. 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](https://twitter.com/suyashysonawane) - suyashsonawane007@gmail.com

Project Link: [https://github.com/suyashsonawane/JavaNet](https://github.com/suyashsonawane/JavaNet)

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JavaNet

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