Incorrect dimensions for matrix multiplication

Question

Ecem 2022-12-20

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1882152-incorrect-dimensions-for-matrix-multiplication

评论： Adam Danz 2022-12-20

clc;clear all;
syms J
% syms x1 x2 x3
N=50;
%MODEL
x = -2*pi:0.1:2*pi;
y = sin(x)./x;
x_t=transpose(x);
t=x_t;
y_t= sin(t)./t; 
t=x_t;
%RANDOMIZATION
relation = [x;y];
ind = 1:length(x);
a = ind(randperm(length(ind)));
shuffeled_vector = relation(:,a);
%NORMALIZATION
factor = max(max(abs(shuffeled_vector(1,:))),max(abs(shuffeled_vector(2,:))));
shuffeled_vector = shuffeled_vector./factor;
%SEPERATION
RATIO = 0.7; % Train-Test Ratio
train_data = shuffeled_vector(:,1:fix(length(x)*RATIO));
test_data = shuffeled_vector(:,fix(length(x)*RATIO)+1:end);
%JACOBIAN MATRIX CALCULATIONS
S = 10; % No. of Neurons
j = 1;
loop = 1;
X = randn(1,3*S+1);
xk=-2+4*X;
syms x [1 3*S+1 ]   
h=(exp(x1)-exp(-x1))/(exp(x1)+exp(-x1)); 
for t_i = train_data(1,:)
    i=1:size(t_i,1);
    
    while loop
        if j <= S
            hs=subs(h,x1,X(j)*t_i+X(S+j));
            Jo(i,j)= X(j+2*S)*t_i*(1-hs^2);
        elseif (j > S) && ( j <= 2*S )
            hs=subs(h,x1,X(j-S)*t_i+X(j));
            Jo(i,j)= X(j+S)*t_i*(1-hs^2);
            
        elseif (j > 2*S) && ( j <= 3*S )
            hs=subs(h,x1,X(j-2*S)*t_i+X(j-S));
            Jo(i,j)= [hs^2];
        else
            Jo(i,j)= 1;
            loop = 0;
        end
        j = j + 1;
    end
    
    A2=0;
    for j=(2*S+1):3*S
        A1=x(j)*subs(h,x1,X(j-2*S)*t(i)+X(j-S));
        A2=A1+A2;
    end
    y_model(i,1)=A2+x(3*S+1);
end
E=vpa(y_t-y_model);
f=vpa(E.'*E);
while 1
    xk_old=xk;
    Nmax=100;
    u=150;
    uscale=0.5; 
    u_min=0.001; 
    u_max=150;
    
    ep1 = 1e-10;
    ep2 = 1e-10;
    ep3 = 1e-10;
    
    [xk]= LevenbergMdeneme(f,xk,E,Jo,u,uscale,u_min,u_max,Nmax,ep1,ep2,ep3); 
    fprintf('Iteration %d of YSA is done\n',k); 
    norm_E=double(norm(subs(E,x,xk))); 
    delta_xk=norm(xk-xk_old); 
    f_MSE=double(subs(f,x,xk)); 
    training_error(k)=log10(norm_E); 
    if norm_E<10^-5 && delta_xk<10^-5 || f_MSE<10^-5|| training_error(k)<-3 || k>N
        fprintf('SISO_ANN Number of Iterations is %d\n',k);
        fprintf('SISO_ANN Training Error is %.4f\n',norm_E);
        fprintf('SISO_ANN Change in ANN Weights is %.4f\n',delta_xk);
        y_model_val=double(subs(y_model,x,xk));
        figure(1);
        subplot(2,1,1);
        plot(t,y_data,'-*');
        hold on
        plot(t,y_model_val,'-o');
        grid on
        title('Train Data Graph');
        xlabel('Input');
        ylabel('Output');
        legend('Real Output of Training Data','Model Output of Train Data')
        
        subplot(2,1,2);
        plot(1:1:k,training_error,'b');
        grid on
        title('Train Error');
        xlabel('Iteration');
        ylabel('log(MSE)');
        break;
    end
    k=k+1;
end
Error using *
Incorrect dimensions for matrix multiplication. Check that the number of columns in the first matrix matches the number of rows in the second matrix. To operate on each element of the matrix
individually, use TIMES (.*) for elementwise multiplication.

Error in solution>LevenbergMdeneme (line 148)
        zk=D*Jx.'*Ex
%---------------------------------------- I called this function
function [xk]=LevenbergMdeneme(f,xk,E,Jo,u,uscale,u_min,u_max,Nmax,ep1,ep2,ep3);
syms x [1 length(xk)];
Nmax=100;
u=150;
uscale=0.5; 
u_min=0.001; 
u_max=150; 
ep1 = 1e-10;
ep2 = 1e-10;
ep3 = 1e-10;
f_grad(x) = gradient(f,x);
x_old=0; 
old_f = 0;
z=size(Jo,2); 
n = length(xk);
i=1; 
while (i < Nmax) && (abs(double(subs(f,x,xk)) - old_f) > ep1) && (norm(xk-x_old) > ep2) && (norm(double(subs(f_grad,x,xk))) > ep3)
    fx=double(subs(f,x,xk));  
    Ex=double(subs(E,x,xk));
    Jx=double(subs(Jo,x,xk));
    
    I=eye(n);
    while 1
        J_xx=Jx.'*Jx+u.*I;
        
        D=-(inv(J_xx));
        
        zk=D*Jx.'*Ex
        
        f_xz=subs(f,x,xk'+zk');
        if f_xz<fx
            pk=zk;
            f_sk=subs(f,x,xk+x1.*pk);
            [sk]=GoldensectionMethod(f_sk,10,-10,10^-4);
            x_old=xk;
            xk=double(xk+sk.*pk);
            u=u/uscale;
            break
        else
            u=u*uscale;
            if u<umax && u>umin
                continue  %go back to start of while  
            else
                break; %end the while
            end
        end
    end
    f1=subs(f,x,x_old);
    f2=subs(f,x,xk);
    normgrad=norm(subs(grad_F,x,xk));
    error=abs(f2-f1);
    delta_x=norm(xk-x_old);
    if i>Nmax
        fprintf('Max iteration = %d\n',i)
        break
    elseif error<ep1
        fprintf('Condition 2 iteration = %d\n',i)
        break
    elseif delta_x<ep2
        fprintf('Condition 3 iteration = %d\n',i)
        break
    elseif normgrad<ep3
        fprintf('Condition 4 iteration = %d\n',i)
        break
    elseif u<u_min || u>u_max
        fprintf('Condition 5 iteration = %d\n',i)
        break;  
    end
    i=i+1;
end
end

1 个评论
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Adam Danz 2022-12-20

As the error message indicates, the problem occurs in this line

zk=D*Jx.'*Ex;

where D*Jx.' is a 31x1 and Ex is 126x1 and those two vectors cannot be multiplied.

If you trace these variables back, you'll find that the 31 comes from the number of neurons (10) x 3 + 1

X = randn(1,3*S+1); % where S is the number of neurons

while the 126 comes from your definition of x in your mode: x = -2*pi:0.1:2*pi; which has 126 values.

Without knowing what the function is supposed to do and without having the time to reverse-engineer it, I'm affraid I can't say more. If you intended to compute a 31x126 matrix, then you could transpose Ex and compute

zk=D*Jx.'*Ex.';

however, the next line will break for the same reason - incorrect matrix dimensions. I recommend using debug mode to step through the function line-by-line to make sure each line is fulfilling its intention.

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