matlab简单神经网络示例

一、模型训练

sample_in = []

sample_out = []

offset = 5

path='D:\demo3\l\';

Files = dir(fullfile(path,'*.png'));

LengthFiles = length(Files);

a_s_out = [1 0 0];

for i=1:LengthFiles

    Img = imread([path,Files(i).name]); 

    Img_grey = rgb2gray(Img);

    a_sample = [];

    for i = 1:offset:size(Img_grey,1)

        for j = 1:offset:size(Img_grey,2)

            a_sample(end+1) = Img_grey(i,j);

        end

    end

    sample_in = [sample_in;a_sample];

    sample_out = [sample_out;a_s_out];

    %plot(a_sample);

    %imagesc(Img2);

end

size(sample_in)

path='D:\demo3\r\';

Files = dir(fullfile(path,'*.png'));

LengthFiles = length(Files);

a_s_out = [0 0 1];

for i=1:LengthFiles

    Img = imread([path,Files(i).name]); 

    Img_grey = rgb2gray(Img);

    a_sample = [];

    for i = 1:offset:size(Img_grey,1)

        for j = 1:offset:size(Img_grey,2)

            a_sample(end+1) = Img_grey(i,j);

        end

    end

    sample_in = [sample_in;a_sample];

    sample_out = [sample_out;a_s_out];

    %plot(a_sample);

    %imagesc(Img2);

end

path='D:\demo3\m\';

Files = dir(fullfile(path,'*.png'));

LengthFiles = length(Files);

a_s_out = [0 1 0];

for i=1:LengthFiles

    Img = imread([path,Files(i).name]); 

    Img_grey = rgb2gray(Img);

    a_sample = [];

    for i = 1:offset:size(Img_grey,1)

        for j = 1:offset:size(Img_grey,2)

            a_sample(end+1) = Img_grey(i,j);

        end

    end

    sample_in = [sample_in;a_sample];

    sample_out = [sample_out;a_s_out];

    %plot(a_sample);

    %imagesc(Img2);

end

 p = sample_in';

 t = sample_out';%[1 0;0 1]';

% % %创建BP网络

 net = newff(minmax(p),[100 3],{'logsig' 'logsig'},'traingda');

% % 

% % %初始化BP网络，包括：权值和偏置的初值

  net = init(net);

% % 

% % %设置训练参数和训练BP网络

  net.trainParam.epochs = 200;

  net.trainParam.goal = 0.001;

  net.trainParam.show = 10;

% % %训练BP神经网络

net = train(net,p,t);

%保存网络

save('jtt_net.mat','net');

%自洽验证，

BPoutput=sim(net,p); 

str = 'BPoutput size is';

size(BPoutput)

figure;

imagesc(BPoutput);

order = [];

for j = 1:size(BPoutput,2)%very sample

    big_order = 1;

    big_val = BPoutput(1,j);

    for i = 2: size(BPoutput,1)

        if big_val < BPoutput(i,j)

            big_order = i;

        end

    end

    order(end+1) = big_order;

end
figure;plot(order,'*');

二、模型使用

load('jtt_net.mat');

sample_in = []

sample_out = []

offset = 5

path='D:\demo3\before\';

Files = dir(fullfile(path,'*.png'));

LengthFiles = length(Files);

for i=1:LengthFiles

    Img = imread([path,Files(i).name]); 

    Img_grey = rgb2gray(Img);

    a_sample = [];

    for i = 1:offset:size(Img_grey,1)

        for j = 1:offset:size(Img_grey,2)

            a_sample(end+1) = Img_grey(i,j);

        end

    end

    sample_in = [sample_in;a_sample];

end

p = sample_in';

size(p)

size(t)

BPoutput=sim(net,p); 

str = 'BPoutput size is';

size(BPoutput)

figure;

imagesc(BPoutput);

order = [];

for j = 1:size(BPoutput,2)%very sample

    big_order = 1;

    big_val = BPoutput(1,j);

    for i = 2: size(BPoutput,1)

        if big_val < BPoutput(i,j)

            big_order = i;

        end

    end

    order(end+1) = big_order;

end

figure;plot(order,'*');

%文件转移

for i=1:LengthFiles

    if order(i) == 1

        copyfile([path,Files(i).name],[path,'\left\',Files(i).name]);

    elseif (order(i)==2)

        copyfile([path,Files(i).name],[path,'\middle\',Files(i).name]);

    elseif (order(i)==3)

        copyfile([path,Files(i).name],[path,'\right\',Files(i).name]);

    end;        

end;