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) ;