MNIST手寫數字識別

Matlab代碼：

%Neural Networks Codes will be run on this part tic %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% clc clear all pic1=imread('8.png'); pic1=rgb2gray(pic1); % pic=255-pic; [a,b]=size(pic1); for i=1:1:afor j=1:1:b if pic1(i,j)==0 up=i; break endend end for i=a:-1:1for j=1:1:b if pic1(i,j)==0 down=i;breakend end end for j=1:1:bfor i=1:1:a if pic1(i,j)==0 left=j;breakend end end for j=b:-1:1for i=1:1:a if pic1(i,j)==0 right=j;breakend end end pic=pic1(down:up,right:left); imshow(pic) pic=imresize(pic,[28 28]); % size(pic); pic1=1-double(reshape(pic,784,1))/255; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%加載待識別圖像 image = loadMNISTImages('train-images'); % 加載樣本圖像共60000，size(image)=784*60000 label = loadMNISTLabels('train-labels');%加載樣本圖像對應標簽 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% label1=zeros(60000,10); j=0; for i=1:1:60000 j=label(i)+1;label1(i,j)=1; end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%=訓練樣本 PR=minmax(image); bpnet=newff(PR,[30 10],{'tansig', 'tansig'}, 'traingd', 'learngdm'); net.epoch=100; net.trainParam.epochs=10;%允許最大訓練步數 net.trainParam.goal=0.001; %訓練目標最小誤差0.001 net.trainParam.show=1; %每間隔100步顯示一次訓練結果 net.trainParam.lr=0.01; %學習速率 bpnet=train(bpnet,image,label1'); bpnet=sim(bpnet,pic1); shuzi=find(bpnet==max(bpnet))-1 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% toc

利用自己在寫字板上寫的字測試：

如果不會下載MNIST 數據，可以訪問：我的GitHub：

https://github.com/WeisongZhao/MNIST_Recognization

里面有不使用MATLAB工具箱的源代碼

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