[STM32F4] 神经网络移植到STM32

load NET3 %网络

clear temp

%% 网络的参数，不过网络结构，{}里面的索引不一样

w{1}=net.IW{1};

w{2}= net.LW{2};

w{3}=net.LW{3,2};

w{4}=net.LW{4,3};

b=net.b;



%%归一化参数

[row,col] = find(net.inputConnect==1);   %获取输入矩阵

ps_Xxmax = net.inputs{row,col}.range(:,2);

ps_Xxmin = net.inputs{row,col}.range(:,1);

ps_Xymax = net.inputs{row,col}.processedRange(:,2);

ps_Xymin = net.inputs{row,col}.processedRange(:,1);

[row,col] = find(net.outputConnect==1);   %获取输入矩阵

ps_Yxmax = net.outputs{row,col}.range(:,2);

ps_Yxmin = net.outputs{row,col}.range(:,1);

ps_Yymax = net.outputs{row,col}.processedRange(:,2);

ps_Yymin = net.outputs{row,col}.processedRange(:,1);

%% 测试输入变量

dataX=[19513.4489795918,20577.612244898,20159.6326530612,20345.1020408163,19241.9387755102,19875.1428571429,17836.8163265306,18450.1734693878,19108.2142857143,17741.193877551,20197.5,17988.5

]';

%% 开始计算

temp{1} = (dataX-ps_Xxmin)./(ps_Xxmax-ps_Xxmin).*(ps_Xymax-ps_Xymin)+ ps_Xymin;   %输入归一化

%% 矩阵计算和激活函数计算

for i=2:4

    temp{i} = tansig_apply( w{i-1}*temp{i-1}+b{i-1} );    % 前numLayers-1循环计算

end

x = w{4}*temp{4}+b{4}         % 最后一层不使用tansig函数 

dataY = (ps_Yxmax-ps_Yxmin).*(x-ps_Yymin)./(ps_Yymax-ps_Yymin)+ps_Yxmin   %反归一化

%%最后是将相应变量输出到txt，为了方便写入KEIL中

for i=1:length(w)

    d=w{i};

    d=d';

    writematrix(d(:)',['w' num2str(i)]);

end



for i=1:length(b)

    d=b{i};

    writematrix(d(:)',['b' num2str(i)]);

end

 writematrix((ps_Xxmax)','ps_Xxmax');

 writematrix((ps_Xxmin)','ps_Xxmin');

 writematrix((ps_Xymax)','ps_Xymax');

 writematrix((ps_Xymin)','ps_Xymin');



 writematrix((ps_Yxmax)','ps_Yxmax');

  writematrix((ps_Yxmin)','ps_Yxmin');

 writematrix((ps_Yymax)','ps_Yymax');

 writematrix((ps_Yymin)','ps_Yymin');

 

function a = tansig_apply(n,~)      %tansig函数，为了能够编译成C

    a = 2 ./ (1 + exp(-2*n)) - 1;

end