卷积神经网络在深度学习中的模式识别能力与代码实现的比较

深度学习之卷积神经网络CNN做手写体识别的VS代码。支持linux版本和VS2012版本。 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 ------ ```cpp #include "tiny_cnn.h" using namespace tiny_cnn; // specify loss-function and optimization-algorithm typedef network CNN; // tanh, 32x32 input, 5x5 window, 1-6 feature-maps convolution convolutional_layer C1(32, 32, 5, 1, 6); // tanh, 28x28 input, 6 feature-maps, 2x2 subsampling average_pooling_layer S2(28, 28, 6, 2); // fully-connected layers fully_connected_layer F3(14*14*6, 120); fully_connected_layer 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.