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**tiny-dnn** is a C++11 implementation of deep learning. It is suitable for deep learning on limited computational resource, embedded systems and IoT devices.
| **`Linux/Mac OS`** | **`Windows`** |
|------------------|-------------|
|[](https://travis-ci.org/tiny-dnn/tiny-dnn)|[](https://ci.appveyor.com/project/tinydnn/tiny-dnn)|
## Table of contents
* [Features](#features)
* [Comparison with other libraries](#comparison-with-other-libraries)
* [Supported networks](#supported-networks)
* [Dependencies](#dependencies)
* [Build](#build)
* [Examples](#examples)
* [Contributing](#contributing)
* [References](#references)
* [License](#license)
* [Gitter rooms](#gitter-rooms)
Check out the [documentation](http://tiny-dnn.readthedocs.io/) for more info.
## What's New
- 2016/11/30 [v1.0.0a3 is released!](https://github.com/tiny-dnn/tiny-dnn/tree/v1.0.0a3)
- 2016/9/14 [tiny-dnn v1.0.0alpha is released!](https://github.com/tiny-dnn/tiny-dnn/releases/tag/v1.0.0a)
- 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!](https://github.com/tiny-dnn/tiny-dnn/releases/tag/v0.1.1)
## Features
- 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++11
- 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](https://github.com/tiny-dnn/tiny-dnn/tree/master/examples/caffe_converter)
- simply implemented
- be a good library for learning neural networks
## Comparison with other libraries
Please see [wiki page](https://github.com/tiny-dnn/tiny-dnn/wiki/Comparison-with-other-libraries).
## Supported networks
### layer-types
- 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
### activation functions
* tanh
* sigmoid
* softmax
* rectified linear(relu)
* leaky relu
* identity
* exponential linear units(elu)
### loss functions
* cross-entropy
* mean squared error
* mean absolute error
* mean absolute error with epsilon range
### optimization algorithms
* stochastic gradient descent (with/without L2 normalization and momentum)
* adagrad
* rmsprop
* adam
## Dependencies
Nothing. All you need is a C++11 compiler.
## Build
tiny-dnn is header-ony, so *there's nothing to build*. If you want to execute sample program or unit tests, you need to install [cmake](https://cmake.org/) and type the following commands:
```
cmake .
```
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:
|options|description|default|additional requirements to use|
|-----|-----|----|----|
|USE_TBB|Use [Intel TBB](https://www.threadingbuildingblocks.org/) for parallelization|OFF1|[Intel TBB](https://www.threadingbuildingblocks.org/)|
|USE_OMP|Use OpenMP for parallelization|OFF1|[OpenMP Compiler](http://openmp.org/wp/openmp-compilers/)|
|USE_SSE|Use Intel SSE instruction set|ON|Intel CPU which supports SSE|
|USE_AVX|Use Intel AVX instruction set|ON|Intel CPU which supports AVX|
|USE_NNPACK|Use NNPACK for convolution operation|OFF|[Acceleration package for neural networks on multi-core CPUs](https://github.com/Maratyszcza/NNPACK)|
|USE_OPENCL|Enable/Disable OpenCL support (experimental)|OFF|[The open standard for parallel programming of heterogeneous systems](https://www.khronos.org/opencl/)|
|USE_LIBDNN|Use Greentea LinDNN for convolution operation with GPU via OpenCL (experimental)|OFF|[An universal convolution implementation supporting CUDA and OpenCL](https://github.com/naibaf7/libdnn)|
|USE_SERIALIZER|Enable model serialization|ON2|-|
|BUILD_TESTS|Build unit tests|OFF3|-|
|BUILD_EXAMPLES|Build example projects|OFF|-|
|BUILD_DOCS|Build documentation|OFF|[Doxygen](http://www.doxygen.org/)|
1 tiny-dnn use c++11 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](https://github.com/google/googletest) 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:
```bash
cmake -DUSE_TBB=ON -DBUILD_TESTS=ON .
```
## Customize configurations
You can edit include/config.h to customize default behavior.
## Examples
construct convolutional neural networks
```cpp
#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 net;
// add layers
net << conv(32, 32, 5, 1, 6) // in:32x32x1, 5x5conv, 6fmaps
<< ave_pool(28, 28, 6, 2) // in:28x28x6, 2x2pooling
<< fc(14 * 14 * 6, 120) // 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 train_labels;
std::vector 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(optimizer, train_images, train_labels, 30, 50);
// save
net.save("net");
// load
// network net2;
// net2.load("net");
}
```
construct multi-layer perceptron(mlp)
```cpp
#include "tiny_dnn/tiny_dnn.h"
using namespace tiny_dnn;
using namespace tiny_dnn::activation;
using namespace tiny_dnn::layers;
void construct_mlp() {
network net;
net << fc(32 * 32, 300)
<< fc(300, 10);
assert(net.in_data_size() == 32 * 32);
assert(net.out_data_size() == 10);
}
```
another way to construct mlp
```cpp
#include "tiny_dnn/tiny_dnn.h"
using namespace tiny_dnn;
using namespace tiny_dnn::activation;
void construct_mlp() {
auto mynet = make_mlp({ 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](https://github.com/tiny-dnn/tiny-dnn/tree/master/examples/mnist) page.
## Contributing
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](CONTRIBUTING.md).
## References
[1] Y. Bengio, [Practical Recommendations for Gradient-Based Training of Deep Architectures.](http://arxiv.org/pdf/1206.5533v2.pdf)
arXiv:1206.5533v2, 2012
[2] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, [Gradient-based learning applied to document recognition.](http://yann.lecun.com/exdb/publis/pdf/lecun-01a.pdf)
Proceedings of the IEEE, 86, 2278-2324.
other useful reference lists:
- [UFLDL Recommended Readings](http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Recommended_Readings)
- [deeplearning.net reading list](http://deeplearning.net/reading-list/)
## License
The BSD 3-Clause License
## Gitter rooms
We have a gitter rooms for discussing new features & QA.
Feel free to join us!
