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MiniDNN

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A header-only C++ library for deep neural networks

// repository documentation

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# MiniDNN

**MiniDNN** is a C++ library that implements a number of popular
deep neural network (DNN) models. It has a mini codebase but is fully functional
to construct different types of feed-forward neural networks. **MiniDNN** is
built on top of [Eigen](http://eigen.tuxfamily.org).

**MiniDNN** is a header-only library implemented purely in C++98, whose only
dependency, **Eigen**, is also header-only. These features make it easy to embed
**MiniDNN** into larger projects with a broad range of compiler support.

This project was largely inspired by the [tiny-dnn](https://github.com/tiny-dnn/tiny-dnn/)
library, a header-only C++14 implementation of deep learning models. What makes
**MiniDNN** different is that **MiniDNN** is based on the high-performance **Eigen**
library for numerical computing, and it has better compiler support.

**MiniDNN** is still quite **experimental** for now. Originally I wrote it with the aim of
studying deep learning and practicing model implementation, but I also find it useful in
my own statistical and machine learning research projects.

## Features

- Able to build feed-forward neural networks with a few lines of code
- Header-only, highly portable
- Fast on CPU
- Modularized and extensible
- Provides detailed documentation that is a resource for learning
- Helps understanding how DNN works
- A wonderful opportunity to learn and practice both the nice and dirty parts of DNN

## Quick Start

The self-explanatory code below is a minimal example to fit a DNN model:

```cpp
#include <MiniDNN.h>

using namespace MiniDNN;

typedef Eigen::MatrixXd Matrix;
typedef Eigen::VectorXd Vector;

int main()
{
    // Set random seed and generate some data
    std::srand(123);
    // Predictors -- each column is an observation
    Matrix x = Matrix::Random(400, 100);
    // Response variables -- each column is an observation
    Matrix y = Matrix::Random(2, 100);

// Construct a network object
    Network net;

// Create three layers
    // Layer 1 -- convolutional, input size 20x20x1, 3 output channels, filter size 5x5
    Layer* layer1 = new Convolutional<ReLU>(20, 20, 1, 3, 5, 5);
    // Layer 2 -- max pooling, input size 16x16x3, pooling window size 3x3
    Layer* layer2 = new MaxPooling<ReLU>(16, 16, 3, 3, 3);
    // Layer 3 -- fully connected, input size 5x5x3, output size 2
    Layer* layer3 = new FullyConnected<Identity>(5 * 5 * 3, 2);

// Add layers to the network object
    net.add_layer(layer1);
    net.add_layer(layer2);
    net.add_layer(layer3);

// Set output layer
    net.set_output(new RegressionMSE());

// Create optimizer object
    RMSProp opt;
    opt.m_lrate = 0.001;

// (Optional) set callback function object
    VerboseCallback callback;
    net.set_callback(callback);

// Initialize parameters with N(0, 0.01^2) using random seed 123
    net.init(0, 0.01, 123);

// Fit the model with a batch size of 100, running 10 epochs with random seed 123
    net.fit(opt, x, y, 100, 10, 123);

// Obtain prediction -- each column is an observation
    Matrix pred = net.predict(x);

// Layer objects will be freed by the network object,
    // so do not manually delete them

return 0;
}
```

To compile and run this example, simply download the source code of **MiniDNN** and
[Eigen](http://bitbucket.org/eigen/eigen/get/3.3.4.tar.gz),
and let the compiler know about their paths. For example:

```bash
g++ -O2 -I/path/to/eigen -I/path/to/MiniDNN/include example.cpp
```

## Documentation

The [API reference](https://yixuan.cos.name/MiniDNN/doc/) page contains the documentation
of **MiniDNN** generated by [Doxygen](http://www.doxygen.org/), including all the class APIs.

## License

**MiniDNN** is an open source project licensed under
[MPL2](https://www.mozilla.org/MPL/2.0/).

MiniDNN

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