Question

我正在为同一数据集实施各种反向传播算法并尝试比较性能。我得到了以下教程的帮助。

https://nl.mathworks.com/help/nnet/ug/choose-a-multilayer-neural-network-training-function.html

我试图绘制：

每个算法的均方误差与执行时间
收敛所需的时间与每种算法的均方误差收敛目标

使用以下代码，创建我的神经网络并愿意知道如何实现上述两个图。

%Data
x=0:0.2:6*pi; y=sin(x);
p=con2seq(x); t=con2seq(y);

% Networks
net1=feedforwardnet(20,'trainlm');
net2=feedforwardnet(20,'traingd');
net2.iw{1,1}=net1.iw{1,1};  %set the same weights and biases for the networks 
net2.lw{2,1}=net1.lw{2,1};
net2.b{1}=net1.b{1};
net2.b{2}=net1.b{2};

%training and simulation
net1.trainParam.epochs=1;  % set the number of epochs for the training 
net2.trainParam.epochs=1;
net1=train(net1,p,t);   % train the networks
net2=train(net2,p,t);
a11=sim(net1,p); a21=sim(net2,p);  % simulate the networks with the input vector p


net1.trainParam.epochs=14;
net2.trainParam.epochs=14;
net1=train(net1,p,t);
net2=train(net2,p,t);
a12=sim(net1,p); a22=sim(net2,p);

net1.trainParam.epochs=985;
net2.trainParam.epochs=985;
net1=train(net1,p,t);
net2=train(net2,p,t);
a13=sim(net1,p); a23=sim(net2,p);

%plots
figure
subplot(3,3,1);
plot(x,y,'bx',x,cell2mat(a11),'r',x,cell2mat(a21),'g'); % plot the sine function and the output of the networks
title('1 epoch');
legend('target','trainlm','traingd');
subplot(3,3,2);
postregm(cell2mat(a11),y); % perform a linear regression analysis and plot the result
subplot(3,3,3);
postregm(cell2mat(a21),y);

%
subplot(3,3,4);
plot(x,y,'bx',x,cell2mat(a12),'r',x,cell2mat(a22),'g');
title('15 epochs');
legend('target','trainlm','traingd');
subplot(3,3,5);
postregm(cell2mat(a12),y);
subplot(3,3,6);
postregm(cell2mat(a22),y);

%
subplot(3,3,7);
plot(x,y,'bx',x,cell2mat(a13),'r',x,cell2mat(a23),'g');
title('1000 epochs');
legend('target','trainlm','traingd');
subplot(3,3,8);
postregm(cell2mat(a13),y);
subplot(3,3,9);
postregm(cell2mat(a23),y);

Answer 1

请注意，如果未指定任何错误度量，则默认使用MSE。

培训时，您可以执行以下操作：

[net tr] = train(net, x, t);

然后绘制tr.perf或tr.tperf或tr.vperf

针对不同反向传播算法的性能比较绘图

1 个答案: