tiny-cnn: A C++11 implementation of deep learning (convolutional neural networks)

tiny-cnn is a C++11 implementation of deep learning (convolutional neural networks).

design principle

fast, without GPU
- with TBB threading and SSE/AVX vectorization
- 98.8% accuracy on MNIST in 13 minutes training (@Core i7-3520M)
header only, policy-based design
small dependency & simple implementation

supported networks

layer-types

fully-connected layer
fully-connected layer (with dropout)
convolutional layer
average pooling layer

activation functions

tanh
sigmoid
rectified linear
identity

loss functions

cross-entropy
mean-squared-error

optimization algorithm

stochastic gradient descent (with/without L2 normalization)
stochastic gradient levenberg marquardt

dependencies

boost C++ library
Intel TBB

sample code

construct convolutional neural networks

#include "tiny_cnn.h"
using namespace tiny_cnn;

// specify loss-function and optimization-algorithm
typedef network<mse, gradient_descent> CNN;

// tanh, 32x32 input, 5x5 window, 1-6 feature-maps convolution
convolutional_layer<CNN, tanh_activation> C1(32, 32, 5, 1, 6);

// tanh, 28x28 input, 6 feature-maps, 2x2 subsampling
average_pooling_layer<CNN, tanh_activation> S2(28, 28, 6, 2);

// fully-connected layers
fully_connected_layer<CNN, sigmoid_activation> F3(14*14*6, 120);
fully_connected_layer<CNN, identity_activation> F4(120, 10);

// connect all
CNN mynet;
mynet.add(&C1); mynet.add(&S2); mynet.add(&F3); mynet.add(&F4);

assert(mynet.in_dim() == 32*32);
assert(mynet.out_dim() == 10);

construct multi-layer perceptron(mlp)

#include "tiny_cnn.h"
using namespace tiny_cnn;

typedef network<mse, gradient_descent> MLP;
fully_connected_layer<MLP, sigmoid_activation> F1(32*32, 300);
fully_connected_layer<MLP, identity_activation> F2(300, 10);

MLP mynet;
mynet.add(&F1); mynet.add(&F2);

assert(mynet.in_dim() == 32*32);
assert(mynet.out_dim() == 10);

another way to construct mlp

#include "tiny_cnn.h"
using namespace tiny_cnn;

auto mynet = make_mlp<mse, gradient_descent, tanh_activation>({ 32*32, 300, 10 });

assert(mynet.in_dim() == 32*32);
assert(mynet.out_dim() == 10);

more sample, read main.cpp

build sample program

gcc(4.6~)

without tbb

./waf configure --BOOST_ROOT=your-boost-root
./waf build

with tbb

./waf configure --TBB --TBB_ROOT=your-tbb-root --BOOST_ROOT=your-boost-root
./waf build

with tbb and SSE/AVX

./waf configure --AVX --TBB --TBB_ROOT=your-tbb-root --BOOST_ROOT=your-boost-root
./waf build


./waf configure --SSE --TBB --TBB_ROOT=your-tbb-root --BOOST_ROOT=your-boost-root
./waf build

or edit inlude/config.h to customize default behavior.

vc(2012~)

open vc/tiny_cnn.sln and build in release mode.

license

The BSD 3-Clause License

reds/tiny-cnn