tiny-dnn is a C 14 implementation of deep learning. It is suitable for deep learning on limited computational resource, embedded systems and IoT devices.

Linux/Mac OS	Windows

• Features
• Comparison with other libraries
• Supported networks
• Dependencies
• Build
• Examples
• Contributing
• References
• License
• Gitter rooms

Check out the documentation for more info.

• 2016/11/30 v1.0.0a3 is released!
• 2016/9/14 tiny-dnn v1.0.0alpha is released!
• 2016/8/7 tiny-dnn is now moved to organization account, and rename into tiny-dnn :)
• 2016/7/27 tiny-dnn v0.1.1 released!

• reasonably fast, without GPU
  • with TBB threading and SSE/AVX vectorization
  • 98.8% accuracy on MNIST in 13 minutes training (@Core i7-3520M)
• portable & header-only
  • Run anywhere as long as you have a compiler which supports C 14
  • Just include tiny_dnn.h and write your model in C . There is nothing to install.
• easy to integrate with real applications
  • no output to stdout/stderr
  • a constant throughput (simple parallelization model, no garbage collection)
  • work without throwing an exception
  • can import caffe's model
• simply implemented
  • be a good library for learning neural networks

Please see wiki page.

• core
  • fully-connected
  • dropout
  • linear operation
  • power
• convolution
  • convolutional
  • average pooling
  • max pooling
  • deconvolutional
  • average unpooling
  • max unpooling
• normalization
  • contrast normalization (only forward pass)
  • batch normalization
• split/merge
  • concat
  • slice
  • elementwise-add

• tanh
• sigmoid
• softmax
• rectified linear(relu)
• leaky relu
• identity
• exponential linear units(elu)

• cross-entropy
• mean squared error
• mean absolute error
• mean absolute error with epsilon range

• stochastic gradient descent (with/without L2 normalization and momentum)
• adagrad
• rmsprop
• adam

Nothing. All you need is a C 14 compiler (gcc 4.9 , clang 3.6 or VS 2015 ).

tiny-dnn is header-only, so there's nothing to build. If you want to execute sample program or unit tests, you need to install cmake and type the following commands:

cmake . -DBUILD_EXAMPLES=ON
make

Then change to examples directory and run executable files.

If you would like to use IDE like Visual Studio or Xcode, you can also use cmake to generate corresponding files:

cmake . -G "Xcode"            # for Xcode users
cmake . -G "NMake Makefiles"  # for Windows Visual Studio users

Then open .sln file in visual studio and build(on windows/msvc), or type make command(on linux/mac/windows-mingw).

Some cmake options are available:

¹ tiny-dnn use C 14 standard library for parallelization by default

² If you don't use serialization, you can switch off to speedup compilation time.

³ tiny-dnn uses Google Test as default framework to run unit tests. No pre-installation required, it's automatically downloaded during CMake configuration.

For example, type the following commands if you want to use intel TBB and build tests:

cmake -DUSE_TBB=ON -DBUILD_TESTS=ON .

You can edit include/config.h to customize default behavior.

construct convolutional neural networks

#include "tiny_dnn/tiny_dnn.h"
using namespace tiny_dnn;
using namespace tiny_dnn::activation;
using namespace tiny_dnn::layers;

void construct_cnn() {
    using namespace tiny_dnn;

    network<sequential> net;

    // add layers
    net << conv(32, 32, 5, 1, 6) << tanh()  // in:32x32x1, 5x5conv, 6fmaps
        << ave_pool(28, 28, 6, 2) << tanh() // in:28x28x6, 2x2pooling
        << fc(14 * 14 * 6, 120) << tanh()   // in:14x14x6, out:120
        << fc(120, 10);                     // in:120,     out:10

    assert(net.in_data_size() == 32 * 32);
    assert(net.out_data_size() == 10);

    // load MNIST dataset
    std::vector<label_t> train_labels;
    std::vector<vec_t> train_images;

    parse_mnist_labels("train-labels.idx1-ubyte", &train_labels);
    parse_mnist_images("train-images.idx3-ubyte", &train_images, -1.0, 1.0, 2, 2);

    // declare optimization algorithm
    adagrad optimizer;

    // train (50-epoch, 30-minibatch)
    net.train<mse, adagrad>(optimizer, train_images, train_labels, 30, 50);

    // save
    net.save("net");

    // load
    // network<sequential> net2;
    // net2.load("net");
}

construct multi-layer perceptron(mlp)

#include "tiny_dnn/tiny_dnn.h"
using namespace tiny_dnn;
using namespace tiny_dnn::activation;
using namespace tiny_dnn::layers;

void construct_mlp() {
    network<sequential> net;

    net << fc(32 * 32, 300) << sigmoid() << fc(300, 10);

    assert(net.in_data_size() == 32 * 32);
    assert(net.out_data_size() == 10);
}

another way to construct mlp

#include "tiny_dnn/tiny_dnn.h"
using namespace tiny_dnn;
using namespace tiny_dnn::activation;

void construct_mlp() {
    auto mynet = make_mlp<tanh>({ 32 * 32, 300, 10 });

    assert(mynet.in_data_size() == 32 * 32);
    assert(mynet.out_data_size() == 10);
}

more sample, read examples/main.cpp or MNIST example page.

Since deep learning community is rapidly growing, we'd love to get contributions from you to accelerate tiny-dnn development! For a quick guide to contributing, take a look at the Contribution Documents.

[1] Y. Bengio, Practical Recommendations for Gradient-Based Training of Deep Architectures. arXiv:1206.5533v2, 2012

[2] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86, 2278-2324.

other useful reference lists:

• UFLDL Recommended Readings
• deeplearning.net reading list

The BSD 3-Clause License

We have a gitter rooms for discussing new features & QA. Feel free to join us!