使用反向传播训练实值神经网络

我想训练一个实际值输出的神经网络，我只是给出了网络插值集（看起来像方波），但是后向传播总是不给我非常适合输入，我试图添加更多的输入值和标准化输出的特性，但它似乎没有帮助。网络是3层1输入1隐藏1输出和1输出节点我如何解决这个问题？我也使用这个成本函数是否正确？使用反向传播训练实值神经网络

for k = 1:m 

    C= C+(y(k)-a2(k))^2; 
end

我的代码：

clc; 
clear all; 
close all; 
input_layer_size = 4; 
hidden_layer_size = 60; 
num_labels = 1; 
load('Xs'); 
load('Y-s'); 
theta1=randInitializeWeights(4, 60); 
theta2=randInitializeWeights(60, 1); 
plot (xq,vq) 
hold on 
xq=polyFeatures(xq,4); 
param=[theta1(:) ;theta2(:)]; 

[J ,Grad]= nnCostFunction(param,input_layer_size ,hidden_layer_size,num_labels,xq,vq,0); 

     options = optimset('MaxIter', 50); 
    costFunction = @(p) nnCostFunction(p, ... 
           input_layer_size, ... 
           hidden_layer_size, ... 
           num_labels, xq, vq, 10); 


    [nn_params, cost] = fmincg(costFunction, param, options); 

Theta1 = reshape(nn_params(1:hidden_layer_size * (input_layer_size + 1)), ... 
      hidden_layer_size, (input_layer_size + 1)); 

Theta2 = reshape(nn_params((1 + (hidden_layer_size * (input_layer_size + 1))):end), ... 
      num_labels, (hidden_layer_size + 1)); 

     l=xq(:,1); 
     out =predictTest(Theta1,Theta2,xq); 

      accuracy=mean(double(out == vq)) * 100 
     plot (l,out,'yellow'); 

     hold off 



    function [J grad] = nnCostFunction(nn_params, ... 
     input_layer_size, ... 
     hidden_layer_size, ... 
     num_labels, ... 
     X, y, lambda) 

    y(841:901)=0; 
    y=y/2.2; 

    Theta1 = reshape((nn_params(1:hidden_layer_size * (input_layer_size+1))), ... 
     hidden_layer_size, (input_layer_size +1)); 

    Theta2 = reshape(nn_params((1+(hidden_layer_size * (input_layer_size +1))):end), ... 
     num_labels, (hidden_layer_size +1)); 

    m = size(X, 1); 
    J = 0; 
    Theta1_grad = zeros(size(Theta1)); 
    Theta2_grad = zeros(size(Theta2)); 


    X= [ones(m,1) X]; 

    z1=X*Theta1'; 
    a1 = sigmoid(z1); 
    a1= [ones(size(a1,1),1) a1]; 
    z2=a1*Theta2'; 
    a2= sigmoid(z2); 


    for k = 1:m 

     J= J+(y(k)-a2(k))^2; 

    end 
    J= J/m; 
    Theta1(:,1)=zeros(1,size(Theta1,1)); 
    Theta2(:,1)=zeros(1,size(Theta2,1)); 
    s1=sum (sum (Theta1.^2)); 
    s2=sum (sum (Theta2.^2)); 

    s3= lambda *(s2 +s1); 
    s3=s3/(2*m); 
    J=J+s3; 

    D2=zeros(size(Theta2)); 
    D1=zeros(size(Theta1)); 
    for i= 1:m 

     delta3=a2(i)-y(i); 
     v=[0 sigmoidGradient(z1(i,:))]; 
     delta2=(Theta2'*delta3').*v'; 



     D2=D2+delta3'*a1(i,:) ; 
     D1=D1+delta2(2:end)*X(i,:); 


    end 


    Theta1_grad = D1./m + (lambda/m)*[zeros(size(Theta1,1), 1) Theta1(:, 2:end)]; 
    Theta2_grad = D2./m + (lambda/m)*[zeros(size(Theta2,1), 1) Theta2(:, 2:end)]; 

    grad = [Theta1_grad(:) ; Theta2_grad(:)]; 


    end 



    function W = randInitializeWeights(L_in, L_out) 


    epsilon_init = 0.5; 
    W = rand(L_out, 1 + L_in)*2*epsilon_init - epsilon_init; 

    end

输入是1：9插0.01增量和目标0之间是数字：2.2像方形脉冲

linear interpolation of data vs predicted in red

updated after increasing epochs

来源

2017-03-09 M.Saeed

欢迎来到Stack Overflow。您能否提供更多关于网络拓扑和一些示例输入和输出数据的信息。还请包括整个算法以及权重初始化。 –

谢谢，我已更新内容 –

您可以为每个输入添加一个预期输出数据的小表吗？ –