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) ;
训练Train
运行cnn_plate.m训练网络,训练过程中的曲线如下图所示,可以看出很快就到达99%的准确率.
测试Demo
demo.m展示了如何使用训练好的模型
Note:记得修改netpath为自己训练的模型哟.