MatConvNet tutorial：Train your own data

用MatConvNet训练自己的数据

安装和编译MatConvNet(Build the library with CUDA)

git clone https://github.com/vlfeat/matconvnet

cd matconvnet

%create a new file called compileGPU.m and save its contents as:

addpath matlab

vl_compilenn('enableGpu', true, ...

           'cudaRoot', 'C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0', ...

           'cudaMethod', 'nvcc');%,...

%                'enableCudnn', 'true',...

%                'cudnnRoot','E:\MachineLearning\DeepLearning\CuDNN\CUDNNv4') ;

%



%then setup the mex environment

%please select VS2015 or greater

mex -setup c

mex -setup cpp

%finally compile it

compileGPU

准备数据Prepare data

在这里从EasyPR获取了车牌数据(解压data.zip即可),0-9共10类字符,每类字符存放在一个子文件夹下,如下图所示：

代码加载数据的部分位于cnn_plate_setup_data.m，请自行调节输入图像大小

inputSize =[20,20,1];

数据存放的路径在startup.m

datadir='data';

编写网络结构Setup the net structure

参考cnn_plate_init.m编写网络结构，构建了3层卷积和池化的网络，激活函数为ReLU.

f=1/100 ;

net.layers = {};

net.layers{end+1} = struct('type', 'conv', ...

                           'weights', {{f*randn(3,3,1,20, 'single'), zeros(1, 20, 'single')}}, ...

                           'stride', 1, ...

                           'pad', 0) ;

net.layers{end+1} = struct('type', 'pool', ...

                           'method', 'max', ...

                           'pool', [2 2], ...

                           'stride', 2, ...

                           'pad', 0) ;

net.layers{end+1} = struct('type', 'relu') ;

net.layers{end+1} = struct('type', 'conv', ...

                           'weights', {{f*randn(3,3,20,100, 'single'),zeros(1,100,'single')}}, ...

                           'stride', 1, ...

                           'pad', 0) ;

net.layers{end+1} = struct('type', 'pool', ...

                           'method', 'max', ...

                           'pool', [2 2], ...

                           'stride', 2, ...

                           'pad', 0) ;

net.layers{end+1} = struct('type', 'relu') ;

net.layers{end+1} = struct('type', 'conv', ...

   'weights', {{f*randn(3,3,100,65, 'single'),zeros(1,65,'single')}}, ...

   'stride', 1, ...

   'pad', 0) ;

net.layers{end+1} = struct('type', 'softmaxloss') ;



% Meta parameters

net.meta.inputSize = [20 20 1] ;

net.meta.trainOpts.learningRate = logspace(-3, -5, 100);

net.meta.trainOpts.numEpochs = 50 ;

net.meta.trainOpts.batchSize = 1000 ;



% Fill in defaul values

net = vl_simplenn_tidy(net) ;