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tiny-cnn: A C++11 implementation of convolutional neural networks

tiny-cnn is a C++11 implementation of convolutional neural networks.

design principle

  • fast, without GPU

98.8% accuracy on MNIST in 13 minutes training (@Core i7-3520M)

  • header only, policy-based design

supported networks

layer-types

  • fully-connected layer
  • 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

#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);

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.