In Simulink, I created two computation modules A and B, as shown in Figure 1. Modules A and B are independent of each other and do not share any data. When A and B are not connected, the time history chart calculated by Module A is shown in Figure 1. However, when A is connected to B, Module A calculates a different time history chart, as shown in Figure 2. Note that Module B does not form a closed-loop with Module A. Why does this situation occur? I would appreciate it if anyone could tell me the reason, thank you very much.
Additionally, I found that as long as module A is connected with the transfer function module (without forming a closed-loop circuit), the time course graph on the oscilloscope will change.In Simulink, I created two computation modules A and B, as shown in Figure 1. Modules A and B are independent of each other and do not share any data. When A and B are not connected, the time history chart calculated by Module A is shown in Figure 1. However, when A is connected to B, Module A calculates a different time history chart, as shown in Figure 2. Note that Module B does not form a closed-loop with Module A. Why does this situation occur? I would appreciate it if anyone could tell me the reason, thank you very much.
Additionally, I found that as long as module A is connected with the transfer function module (without forming a closed-loop circuit), the time course graph on the oscilloscope will change. In Simulink, I created two computation modules A and B, as shown in Figure 1. Modules A and B are independent of each other and do not share any data. When A and B are not connected, the time history chart calculated by Module A is shown in Figure 1. However, when A is connected to B, Module A calculates a different time history chart, as shown in Figure 2. Note that Module B does not form a closed-loop with Module A. Why does this situation occur? I would appreciate it if anyone could tell me the reason, thank you very much.
Additionally, I found that as long as module A is connected with the transfer function module (without forming a closed-loop circuit), the time course graph on the oscilloscope will change. module, interact, simulink MATLAB Answers — New Questions

​

I am writting a program the finds the polynomial using the least-square method and Gausian elimination and from there displays the correct polynomial equation. The code for the least-square and the Gaussian elimination works fine, where I am having trouble is at the end getting the display of the polynomial equation depending on what value of "n" you show correcty and nothing lower then or higher then the corresponding the chosen value of "n". What loop would for best to do this? My fist thought is and if else loop but how would I set this up to run correctly?
clear, close, clc
%input
x=[0 1 2 3 4 5];
y=[2.1 7.7 13.6 27.2 40.9 61.1];
n=8; %order of the polynomial you want to find

%%%LEAST SQUARE METHOD-FINDS POLYNOMIAL FOR GIVEN DATA SET%%%%%

%INPUT SECTION
x=x; %x-cordinates from data-input; independent vairiables
y=y; %y-cordinates from data-output; dependent vairiables

%CALCULATIONS SECTION
k=length(x); %NUMBER OF AVAILABLE DATA POINTS
m=n+1; %SIZE OF THE COEFFICENT MATRIX
A=zeros(m,m); %COEFFICENT MATRIX

for j=1:m
for i=1:m
A(j,i)=sum(x.^(i+j-2));
end
end

B=zeros(m,1); %FORCING FUNCTION VECTOR

for i=1:m;
B(i)=sum(y.*x.^(i-1));
end

a1=AB %COEFFICIENTS FOR THE POLYNOMINAL–> y=a0+a1*x+a2*x^2….an*x^n CAN BE REPLACED BY GAUSSIAN ELIMINATION

%%%%%=========GAUSSIAN ELIMINATION TO FIND "a"========%%%%%%
%%%INPUT SECTION

%CALCULATION SECTION
AB=[A B]; %Augumentent matrix
R=size(AB,1); %# OF ROWS IN AB
C=size(AB,2); %# OF COLUMNS IN AB

%%%%FOWARD ELIMINATION SECTION

for J=1:R-1
[M,I]=max(abs(AB(J:R,J))); %M=MAXIMUM VALUE, I=LOCATION OF THE MAXIMUM VALUE IN THE 1ST ROW
temp=AB(J,:);
AB(J,:)=AB(I+(J-1),:);
AB(I+(J-1),:)=temp;

for i=(J+1):R;
if AB(i,J)~=0;
AB(i,:)=AB(i,:)-(AB(i,J)/AB(J,J))*AB(J,:);
end
end
end

%%%%BACKWARDS SUBSTITUTION
a(R)=AB(R,C)/AB(R,R);

for i=R-1:-1:1
a(i)=(AB(i,C)-AB(i,i+1:R)*a(i+1:R)’)/AB(i,i);
end

disp(a)
%========END OF GAUSSIAN ELIMINATION=======%%%%%%%%

%STANDARD DEVIATION
Y_bar=mean(y); %ADVERAGE OF y
St=sum((y-Y_bar).^2);
SD=sqrt(St/(k-1)); %STANDARD DEVIATION

%STANDARD ERROR

for i=1:m;
T(:,i)=a(i)*x.^(i-1); %T=INDIVIDUAL POLYNOMIAL TERMS
end

for i=1:k
y_hat(i)=sum(T(i,:));
end

Sr=sum((y-y_hat).^2);

Se=sqrt(Sr/(k-(n+1))); %STANDARD ERROR-Se

%COEFFICIENT OF DETERMINATION
Cd=(St-Sr)/St %COEFFICIENT OF DETERMINATION (r^2)
fprintf(‘For n=%d. Coefficient of Determination=%0.5fn’,n,Cd)
%EQUATION FOR THE POLYNOMIAL
syms x y

for n=1
a0=a(:,1);a1=a(:,2);a2=a(:,3);
sympref("FloatingPointOutput",true);
y=a0+a1*x+a2*x.^2
end

for n=2
a0=a(:,1);a1=a(:,2);a2=a(:,3);a3=a(:,4);
sympref("FloatingPointOutput",true);
y=a0+a1*x+a2*x.^2+a3*x.^3
end

for n=3
a0=a(:,1);a1=a(:,2);a2=a(:,2);a3=a(:,4);a4=a(:,5);
sympref("FloatingPointOutput",true);
y=a0+a1*x+a2*x.^2+a3*x.^3+a4*x.^4
end

for n=4
a0=a(:,1);a1=a(:,2);a2=a(:,3);a3=a(:,4);a4=a(:,5);a5=a(:,6);
sympref("FloatingPointOutput",true);
y=a0+a1*x+a2*x.^2+a3*x.^3+a4*x.^4+a5*x.^5
end

for n=5
a0=a(:,1);a1=a(:,2);a2=a(:,3);a3=a(:,4);a4=a(:,5);a5=a(:,6);a6=a(:,7)
sympref("FloatingPointOutput",true);
y=a0+a1*x+a2*x.^2+a3*x.^3+a4*x.^4+a5*x.^5+a6*x.^6
end

for n=6
a0=a(:,1);a1=a(:,2);a2=a(:,3);a3=a(:,4);a4=a(:,5);a5=a(:,6);a6=a(:,7);a7=a(:,8);
sympref("FloatingPointOutput",true);
y=a0+a1*x+a2*x.^2+a3*x.^3+a4*x.^4+a5*x.^5+a6*x.^6+a7*x.^7
end

for n=7
a0=a(:,1);a1=a(:,2);a2=a(:,3);a3=a(:,4);a4=a(:,5);a5=a(:,6);a6=a(:,7);a7=a(:,8);a8=a(:,9);
sympref("FloatingPointOutput",true);
y=a0+a1*x+a2*x.^2+a3*x.^3+a4*x.^4+a5*x.^5+a6*x.^6+a7*x.^7+a8*x.^8
end

for n=8
a0=a(:,1);a1=a(:,2);a2=a(:,3);a3=a(:,4);a4=a(:,5);a5=a(:,6);a6=a(:,7);a7=a(:,8);a8=a(:,9);a9=a(:,10);
sympref("FloatingPointOutput",true);
y=a0+a1*x+a2*x.^2+a3*x.^3+a4*x.^4+a5*x.^5+a6*x.^6+a7*x.^7+a8*x.^8+a9*x.^9
end

for n=9
a0=a(:,1);a1=a(:,2);a2=a(:,3);a3=a(:,4);a4=a(:,5);a5=a(:,6);a6=a(:,7);a7=a(:,8);a8=a(:,9);a9=a(:,10);a10=a(:,11);
sympref("FloatingPointOutput",true);
y=a0+a1*x+a2*x.^2+a3*x.^3+a4*x.^4+a5*x.^5+a6*x.^6+a7*x.^7+a8*x.^8+a9*x.^9+a10*x.^10
endI am writting a program the finds the polynomial using the least-square method and Gausian elimination and from there displays the correct polynomial equation. The code for the least-square and the Gaussian elimination works fine, where I am having trouble is at the end getting the display of the polynomial equation depending on what value of "n" you show correcty and nothing lower then or higher then the corresponding the chosen value of "n". What loop would for best to do this? My fist thought is and if else loop but how would I set this up to run correctly?
clear, close, clc
%input
x=[0 1 2 3 4 5];
y=[2.1 7.7 13.6 27.2 40.9 61.1];
n=8; %order of the polynomial you want to find

%%%LEAST SQUARE METHOD-FINDS POLYNOMIAL FOR GIVEN DATA SET%%%%%

%INPUT SECTION
x=x; %x-cordinates from data-input; independent vairiables
y=y; %y-cordinates from data-output; dependent vairiables

%CALCULATIONS SECTION
k=length(x); %NUMBER OF AVAILABLE DATA POINTS
m=n+1; %SIZE OF THE COEFFICENT MATRIX
A=zeros(m,m); %COEFFICENT MATRIX

for j=1:m
for i=1:m
A(j,i)=sum(x.^(i+j-2));
end
end

B=zeros(m,1); %FORCING FUNCTION VECTOR

for i=1:m;
B(i)=sum(y.*x.^(i-1));
end

a1=AB %COEFFICIENTS FOR THE POLYNOMINAL–> y=a0+a1*x+a2*x^2….an*x^n CAN BE REPLACED BY GAUSSIAN ELIMINATION

%%%%%=========GAUSSIAN ELIMINATION TO FIND "a"========%%%%%%
%%%INPUT SECTION

%CALCULATION SECTION
AB=[A B]; %Augumentent matrix
R=size(AB,1); %# OF ROWS IN AB
C=size(AB,2); %# OF COLUMNS IN AB

%%%%FOWARD ELIMINATION SECTION

for J=1:R-1
[M,I]=max(abs(AB(J:R,J))); %M=MAXIMUM VALUE, I=LOCATION OF THE MAXIMUM VALUE IN THE 1ST ROW
temp=AB(J,:);
AB(J,:)=AB(I+(J-1),:);
AB(I+(J-1),:)=temp;

for i=(J+1):R;
if AB(i,J)~=0;
AB(i,:)=AB(i,:)-(AB(i,J)/AB(J,J))*AB(J,:);
end
end
end

%%%%BACKWARDS SUBSTITUTION
a(R)=AB(R,C)/AB(R,R);

for i=R-1:-1:1
a(i)=(AB(i,C)-AB(i,i+1:R)*a(i+1:R)’)/AB(i,i);
end

disp(a)
%========END OF GAUSSIAN ELIMINATION=======%%%%%%%%

%STANDARD DEVIATION
Y_bar=mean(y); %ADVERAGE OF y
St=sum((y-Y_bar).^2);
SD=sqrt(St/(k-1)); %STANDARD DEVIATION

%STANDARD ERROR

for i=1:m;
T(:,i)=a(i)*x.^(i-1); %T=INDIVIDUAL POLYNOMIAL TERMS
end

for i=1:k
y_hat(i)=sum(T(i,:));
end

Sr=sum((y-y_hat).^2);

Se=sqrt(Sr/(k-(n+1))); %STANDARD ERROR-Se

%COEFFICIENT OF DETERMINATION
Cd=(St-Sr)/St %COEFFICIENT OF DETERMINATION (r^2)
fprintf(‘For n=%d. Coefficient of Determination=%0.5fn’,n,Cd)
%EQUATION FOR THE POLYNOMIAL
syms x y

for n=1
a0=a(:,1);a1=a(:,2);a2=a(:,3);
sympref("FloatingPointOutput",true);
y=a0+a1*x+a2*x.^2
end

for n=2
a0=a(:,1);a1=a(:,2);a2=a(:,3);a3=a(:,4);
sympref("FloatingPointOutput",true);
y=a0+a1*x+a2*x.^2+a3*x.^3
end

for n=3
a0=a(:,1);a1=a(:,2);a2=a(:,2);a3=a(:,4);a4=a(:,5);
sympref("FloatingPointOutput",true);
y=a0+a1*x+a2*x.^2+a3*x.^3+a4*x.^4
end

for n=4
a0=a(:,1);a1=a(:,2);a2=a(:,3);a3=a(:,4);a4=a(:,5);a5=a(:,6);
sympref("FloatingPointOutput",true);
y=a0+a1*x+a2*x.^2+a3*x.^3+a4*x.^4+a5*x.^5
end

for n=5
a0=a(:,1);a1=a(:,2);a2=a(:,3);a3=a(:,4);a4=a(:,5);a5=a(:,6);a6=a(:,7)
sympref("FloatingPointOutput",true);
y=a0+a1*x+a2*x.^2+a3*x.^3+a4*x.^4+a5*x.^5+a6*x.^6
end

for n=6
a0=a(:,1);a1=a(:,2);a2=a(:,3);a3=a(:,4);a4=a(:,5);a5=a(:,6);a6=a(:,7);a7=a(:,8);
sympref("FloatingPointOutput",true);
y=a0+a1*x+a2*x.^2+a3*x.^3+a4*x.^4+a5*x.^5+a6*x.^6+a7*x.^7
end

for n=7
a0=a(:,1);a1=a(:,2);a2=a(:,3);a3=a(:,4);a4=a(:,5);a5=a(:,6);a6=a(:,7);a7=a(:,8);a8=a(:,9);
sympref("FloatingPointOutput",true);
y=a0+a1*x+a2*x.^2+a3*x.^3+a4*x.^4+a5*x.^5+a6*x.^6+a7*x.^7+a8*x.^8
end

for n=8
a0=a(:,1);a1=a(:,2);a2=a(:,3);a3=a(:,4);a4=a(:,5);a5=a(:,6);a6=a(:,7);a7=a(:,8);a8=a(:,9);a9=a(:,10);
sympref("FloatingPointOutput",true);
y=a0+a1*x+a2*x.^2+a3*x.^3+a4*x.^4+a5*x.^5+a6*x.^6+a7*x.^7+a8*x.^8+a9*x.^9
end

for n=9
a0=a(:,1);a1=a(:,2);a2=a(:,3);a3=a(:,4);a4=a(:,5);a5=a(:,6);a6=a(:,7);a7=a(:,8);a8=a(:,9);a9=a(:,10);a10=a(:,11);
sympref("FloatingPointOutput",true);
y=a0+a1*x+a2*x.^2+a3*x.^3+a4*x.^4+a5*x.^5+a6*x.^6+a7*x.^7+a8*x.^8+a9*x.^9+a10*x.^10
end I am writting a program the finds the polynomial using the least-square method and Gausian elimination and from there displays the correct polynomial equation. The code for the least-square and the Gaussian elimination works fine, where I am having trouble is at the end getting the display of the polynomial equation depending on what value of "n" you show correcty and nothing lower then or higher then the corresponding the chosen value of "n". What loop would for best to do this? My fist thought is and if else loop but how would I set this up to run correctly?
clear, close, clc
%input
x=[0 1 2 3 4 5];
y=[2.1 7.7 13.6 27.2 40.9 61.1];
n=8; %order of the polynomial you want to find

%%%LEAST SQUARE METHOD-FINDS POLYNOMIAL FOR GIVEN DATA SET%%%%%

%INPUT SECTION
x=x; %x-cordinates from data-input; independent vairiables
y=y; %y-cordinates from data-output; dependent vairiables

%CALCULATIONS SECTION
k=length(x); %NUMBER OF AVAILABLE DATA POINTS
m=n+1; %SIZE OF THE COEFFICENT MATRIX
A=zeros(m,m); %COEFFICENT MATRIX

for j=1:m
for i=1:m
A(j,i)=sum(x.^(i+j-2));
end
end

B=zeros(m,1); %FORCING FUNCTION VECTOR

for i=1:m;
B(i)=sum(y.*x.^(i-1));
end

a1=AB %COEFFICIENTS FOR THE POLYNOMINAL–> y=a0+a1*x+a2*x^2….an*x^n CAN BE REPLACED BY GAUSSIAN ELIMINATION

%%%%%=========GAUSSIAN ELIMINATION TO FIND "a"========%%%%%%
%%%INPUT SECTION

%CALCULATION SECTION
AB=[A B]; %Augumentent matrix
R=size(AB,1); %# OF ROWS IN AB
C=size(AB,2); %# OF COLUMNS IN AB

%%%%FOWARD ELIMINATION SECTION

for J=1:R-1
[M,I]=max(abs(AB(J:R,J))); %M=MAXIMUM VALUE, I=LOCATION OF THE MAXIMUM VALUE IN THE 1ST ROW
temp=AB(J,:);
AB(J,:)=AB(I+(J-1),:);
AB(I+(J-1),:)=temp;

for i=(J+1):R;
if AB(i,J)~=0;
AB(i,:)=AB(i,:)-(AB(i,J)/AB(J,J))*AB(J,:);
end
end
end

%%%%BACKWARDS SUBSTITUTION
a(R)=AB(R,C)/AB(R,R);

for i=R-1:-1:1
a(i)=(AB(i,C)-AB(i,i+1:R)*a(i+1:R)’)/AB(i,i);
end

disp(a)
%========END OF GAUSSIAN ELIMINATION=======%%%%%%%%

%STANDARD DEVIATION
Y_bar=mean(y); %ADVERAGE OF y
St=sum((y-Y_bar).^2);
SD=sqrt(St/(k-1)); %STANDARD DEVIATION

%STANDARD ERROR

for i=1:m;
T(:,i)=a(i)*x.^(i-1); %T=INDIVIDUAL POLYNOMIAL TERMS
end

for i=1:k
y_hat(i)=sum(T(i,:));
end

Sr=sum((y-y_hat).^2);

Se=sqrt(Sr/(k-(n+1))); %STANDARD ERROR-Se

%COEFFICIENT OF DETERMINATION
Cd=(St-Sr)/St %COEFFICIENT OF DETERMINATION (r^2)
fprintf(‘For n=%d. Coefficient of Determination=%0.5fn’,n,Cd)
%EQUATION FOR THE POLYNOMIAL
syms x y

for n=1
a0=a(:,1);a1=a(:,2);a2=a(:,3);
sympref("FloatingPointOutput",true);
y=a0+a1*x+a2*x.^2
end

for n=2
a0=a(:,1);a1=a(:,2);a2=a(:,3);a3=a(:,4);
sympref("FloatingPointOutput",true);
y=a0+a1*x+a2*x.^2+a3*x.^3
end

for n=3
a0=a(:,1);a1=a(:,2);a2=a(:,2);a3=a(:,4);a4=a(:,5);
sympref("FloatingPointOutput",true);
y=a0+a1*x+a2*x.^2+a3*x.^3+a4*x.^4
end

for n=4
a0=a(:,1);a1=a(:,2);a2=a(:,3);a3=a(:,4);a4=a(:,5);a5=a(:,6);
sympref("FloatingPointOutput",true);
y=a0+a1*x+a2*x.^2+a3*x.^3+a4*x.^4+a5*x.^5
end

for n=5
a0=a(:,1);a1=a(:,2);a2=a(:,3);a3=a(:,4);a4=a(:,5);a5=a(:,6);a6=a(:,7)
sympref("FloatingPointOutput",true);
y=a0+a1*x+a2*x.^2+a3*x.^3+a4*x.^4+a5*x.^5+a6*x.^6
end

for n=6
a0=a(:,1);a1=a(:,2);a2=a(:,3);a3=a(:,4);a4=a(:,5);a5=a(:,6);a6=a(:,7);a7=a(:,8);
sympref("FloatingPointOutput",true);
y=a0+a1*x+a2*x.^2+a3*x.^3+a4*x.^4+a5*x.^5+a6*x.^6+a7*x.^7
end

for n=7
a0=a(:,1);a1=a(:,2);a2=a(:,3);a3=a(:,4);a4=a(:,5);a5=a(:,6);a6=a(:,7);a7=a(:,8);a8=a(:,9);
sympref("FloatingPointOutput",true);
y=a0+a1*x+a2*x.^2+a3*x.^3+a4*x.^4+a5*x.^5+a6*x.^6+a7*x.^7+a8*x.^8
end

for n=8
a0=a(:,1);a1=a(:,2);a2=a(:,3);a3=a(:,4);a4=a(:,5);a5=a(:,6);a6=a(:,7);a7=a(:,8);a8=a(:,9);a9=a(:,10);
sympref("FloatingPointOutput",true);
y=a0+a1*x+a2*x.^2+a3*x.^3+a4*x.^4+a5*x.^5+a6*x.^6+a7*x.^7+a8*x.^8+a9*x.^9
end

for n=9
a0=a(:,1);a1=a(:,2);a2=a(:,3);a3=a(:,4);a4=a(:,5);a5=a(:,6);a6=a(:,7);a7=a(:,8);a8=a(:,9);a9=a(:,10);a10=a(:,11);
sympref("FloatingPointOutput",true);
y=a0+a1*x+a2*x.^2+a3*x.^3+a4*x.^4+a5*x.^5+a6*x.^6+a7*x.^7+a8*x.^8+a9*x.^9+a10*x.^10
end loop to display equation for changing input, loop MATLAB Answers — New Questions

​

How we can use multichannel spectrum analysis with time series contain temporal and spatial data :time, latitude , longitude ?
the time is daily-based data , lat-log is 2.5x 5 degrees ???How we can use multichannel spectrum analysis with time series contain temporal and spatial data :time, latitude , longitude ?
the time is daily-based data , lat-log is 2.5x 5 degrees ??? How we can use multichannel spectrum analysis with time series contain temporal and spatial data :time, latitude , longitude ?
the time is daily-based data , lat-log is 2.5x 5 degrees ??? multichannel singular spectrum MATLAB Answers — New Questions

​

I am sweeping hyper parameters used for training an RL agent. everything is fine until I try to use the "Stop" botton on the top to stop the whole experiment, or when I click on "stop" button of each of the individual trainings. It stucks, and in the background I can see the timer for that training still increasing. Even when I try to close the manager using the X button, it tells me "Experiment is currently running" Stop running the experiment?" what is the problem here. It looks like a bug in the software to me.I am sweeping hyper parameters used for training an RL agent. everything is fine until I try to use the "Stop" botton on the top to stop the whole experiment, or when I click on "stop" button of each of the individual trainings. It stucks, and in the background I can see the timer for that training still increasing. Even when I try to close the manager using the X button, it tells me "Experiment is currently running" Stop running the experiment?" what is the problem here. It looks like a bug in the software to me. I am sweeping hyper parameters used for training an RL agent. everything is fine until I try to use the "Stop" botton on the top to stop the whole experiment, or when I click on "stop" button of each of the individual trainings. It stucks, and in the background I can see the timer for that training still increasing. Even when I try to close the manager using the X button, it tells me "Experiment is currently running" Stop running the experiment?" what is the problem here. It looks like a bug in the software to me. experiment manager, hyper parameter sweep MATLAB Answers — New Questions

​

I am tryeing to train neural network training images.
My code is based on this article: https://blogs.mathworks.com/deep-learning/2021/05/10/semantic-segmentation-for-medical-imaging/#respond

Here is complete code of my script:
% Clear workspace
clear; close all; clc;

% Specify the directory path you want to delete
directoryPath = ‘training_images/resized’;

% Check if the directory exists before trying to delete it
if exist(directoryPath, ‘dir’)
% Remove the directory and its contents
[status, message, messageId] = rmdir(directoryPath, ‘s’);

% Check if the operation was successful
if status
disp([‘Directory "’, directoryPath, ‘" has been deleted successfully.’]);
else
disp([‘Failed to delete "’, directoryPath, ‘": ‘, message]);
end
else
disp([‘Directory "’, directoryPath, ‘" does not exist.’]);
end

% All images
imds = imageDatastore( …
‘training_images’, …
‘IncludeSubfolders’, …
true …
);

% Define class names and their corresponding IDs
classNames = ["Lesion","Background"];
labelIDs =[255,0];

% Create a pixelLabelDatastore holding the ground truth pixel labels
pxds=pixelLabelDatastore( …
‘training_images’, …
classNames, …
labelIDs …
);

% Create a pixel label image datastore of all images
pximds=pixelLabelImageDatastore(imds,pxds);

% Number of Images
total_num_images=length(pximds.Images);

% Visualize random images
perm=randperm(total_num_images,4);
figure;

% Visualize the images with Mask

for idx = 1:length(perm)
% Read the image from imds
image = readimage(imds, perm(idx));

% Read the corresponding mask from pxds
mask = readimage(pxds, perm(idx));

% Display the image
subplot(2,2,idx);
imshow(image);
hold on;

binaryMask = mask(:,:,1) == classNames(1);

% Visualize boundaries on the binary mask
visboundaries(binaryMask, ‘Color’, ‘r’);

% Extract the filename for the title from imds
[~, filename] = fileparts(imds.Files{perm(idx)});
title(sprintf(‘%s’, filename), ‘Interpreter’, "none");
end

% Desired Image Size
imageSize=[224 224 3];

% Create a pixel label image datastore of all resized images

% Specify directories for resized images and labels
resizedImagesDir = ‘training_images/images-segmantation/resized/images’;
resizedLabelsDir = ‘training_images/images-segmantation/resized/labels’;

% Ensure the directories exist
if ~exist(resizedImagesDir, ‘dir’), mkdir(resizedImagesDir); end
if ~exist(resizedLabelsDir, ‘dir’), mkdir(resizedLabelsDir); end

% Resize and save images
for i = 1:length(imds.Files)
img = imread(imds.Files{i});
resizedImg = imresize(img, ‘OutputSize’, imageSize(1:2));
[~, fileName, ext] = fileparts(imds.Files{i});
imwrite(resizedImg, fullfile(resizedImagesDir, [fileName, ext]));
end

% Resize and save labels
for i = 1:length(pxds.Files)
label = imread(pxds.Files{i});
resizedLabel = imresize(label, ‘OutputSize’, imageSize(1:2), ‘Method’, ‘nearest’);
[~, fileName, ext] = fileparts(pxds.Files{i});
imwrite(resizedLabel, fullfile(resizedLabelsDir, [fileName, ext]));
end

% Create new ImageDatastore and PixelLabelDatastore from the resized data
imdsResized = imageDatastore(resizedImagesDir);
pximdsResz = pixelLabelDatastore(resizedLabelsDir, classNames, labelIDs);

% Clear all variables except the necessary variables
% clearvars -except pximdsResz classNames total_num_images imageSize

% Split the dataset into training, validation, and testing sets
numImages = numel(imdsResized.Files);

% Adjust the number as needed
testIdx = randperm(numImages, 5);
trainValidIdx = setdiff(1:numImages, testIdx);

% Adjust the number as needed
validIdx = trainValidIdx(randperm(length(trainValidIdx), 10));
trainIdx = trainValidIdx;

% Create datastores for training, validation, and testing sets
imdsTrain = subset(imdsResized, trainIdx);
imdsValid = subset(imdsResized, validIdx);
imdsTest = subset(imdsResized, testIdx);

pximdsTrain = subset(pximdsResz, trainIdx);
pximdsValid = subset(pximdsResz, validIdx);
pximdsTest = subset(pximdsResz, testIdx);

% Combine the validation image datastore with the pixel label datastore
% ValidationData expects a cell array with these combined datastores
validDS = combine(imdsValid, pximdsValid);

% As of MATLAB R2021a, this might not be necessary, and you can directly use validDS
validationData = {validDS};

% Applying the corrected transformation
trainDSConverted = transform(pximdsTrain, @(c) categoricalToNumeric(c{1}, classNames));
validDSConverted = transform(pximdsValid, @(c) categoricalToNumeric(c{1}, classNames));

% trainDSConverted2 = transform(trainDS, @(data) transformData(data));
% validDSConverted2 = transform(validDS, @(data) transformData(data));

% trainDSConverted3 = transform(trainDS, @transformData2);
% validDSConverted3 = transform(validDS, @transformData2);

% For imageDatastore
imdsTrain = subset(imdsResized, trainIdx);
imdsValid = subset(imdsResized, validIdx);
imdsTest = subset(imdsResized, testIdx);

% For pixelLabelDatastore
pxdsTrain = subset(pximdsResz, trainIdx);
pxdsValid = subset(pximdsResz, validIdx);
pxdsTest = subset(pximdsResz, testIdx);

pximdsTrain = pixelLabelImageDatastore(imdsTrain, pxdsTrain);
pximdsValid = pixelLabelImageDatastore(imdsValid, pxdsValid);

% Specify network architecture
numClasses = numel(classNames);

% Adjust based on your actual image size
imageSize = [224 224 3];

% Example using ResNet-18 backbone
lgraph = deeplabv3plusLayers(imageSize, numClasses, ‘resnet50’);

% Define the parameters for the network
options = trainingOptions(‘sgdm’, …
‘InitialLearnRate’, 0.03, …
‘Momentum’, 0.9, …
‘L2Regularization’, 0.0005, …
‘MaxEpochs’, 20, …
‘MiniBatchSize’, 32, …
‘VerboseFrequency’, 20, …
‘LearnRateSchedule’, ‘piecewise’, …
‘ExecutionEnvironment’, ‘cpu’, …
‘Shuffle’, ‘every-epoch’, …
‘ValidationData’, validDSConverted, … % Correct specification of ValidationData
‘ValidationFrequency’, 50, …
‘ValidationPatience’, 4, …
‘Plots’, ‘training-progress’, …
‘GradientThresholdMethod’, ‘l2norm’, …
‘GradientThreshold’, 0.05);

% Train the network
net = trainNetwork(trainDSConverted, lgraph, options);

% Semantic segmentation of test dataset based on the trained network
[pxdspredicted]=semanticseg(pximdsTest,net,’WriteLocation’,tempdir);

% Evaluation
metrics=evaluateSemanticSegmentation(pxdspredicted,pximdsTest);

% Normalized Confusion Matrix
normConfMatData = metrics.NormalizedConfusionMatrix.Variables;
figure
h=heatmap(classNames,classNames,100*normConfMatData);
h.XLabel=’Predicted Class’;
h.YLabel=’True Class’;
h.Title=’Normalized Confusion Matrix (%)’;

% Number of Images
num_test_images=length(pximdsTest.Images);

% Pick any random 2 images
perm=randperm(num_test_images,2);

% Visualize the images with Mask
for idx=1:length(perm)

% Extract filename for the title
[~,filename]=fileparts(pximdsTest.Images{idx});

% Read the original file and resize it for network purposes
I = imread(pximdsTest.Images{perm(idx)});
I = imresize(I,[imageSize(1) imageSize(2)],’bilinear’);

figure;
image(I);
hold on;

% Read the actual mask and resize it for visualization
actual_mask=imread(pximdsTest.PixelLabelData{perm(idx)});
actual_mask=imresize(actual_mask,[imageSize(1) imageSize(2)],’bilinear’);

% Ground Truth
visboundaries(actual_mask,’Color’,’r’);

% Predicted by the Algorithm
predicted_image=(uint8(readimage(pxdspredicted,perm(idx)))); % Values are 1 and 2
predicted_results=uint8(~(predicted_image-1)); % Conversion to binary and reverse the polarity to match with the labelIds
visboundaries(predicted_results,’Color’,’g’);
title(sprintf(‘%s Red- Actual, Green – Predicted’,filename),’Interpreter’,"none");
imwrite(mat2gray(predicted_results),sprintf(‘%s.png’,filename));

end

% Corrected transformation function

function outImg = categoricalToNumeric(inCategorical, classNames)
outImg = zeros(size(inCategorical,1), size(inCategorical,2), ‘uint8’);
for k = 1:length(classNames)
outImg(inCategorical == classNames{k}) = k-1;
end
end

function [img, label] = transformData(data)
% Assumes data is a cell with {image, label} format
img = data{1}; % Image data
inCategorical = data{2}; % Categorical label data
classNames = ["Lesion","Background"]; % Class names as used previously

outImg = zeros(size(inCategorical,1), size(inCategorical,2), ‘uint8’);
for k = 1:length(classNames)
outImg(inCategorical == classNames(k)) = k-1;
end
label = outImg; % Numeric label data
end

function dataOut = transformData2(dataIn)
img = dataIn{1}; % Assuming dataIn{1} is the image
label = dataIn{2}; % Assuming dataIn{2} is the label in numeric format

% Your conversion logic here, ending with…
labelCategorical = categorical(label, 0:max(label(:)), {‘Background’, ‘Lesion’});

dataOut = {img, labelCategorical};
end

When I run it, I get an error at this part:
% Define the parameters for the network
options = trainingOptions(‘sgdm’, …
‘InitialLearnRate’, 0.03, …
‘Momentum’, 0.9, …
‘L2Regularization’, 0.0005, …
‘MaxEpochs’, 20, …
‘MiniBatchSize’, 32, …
‘VerboseFrequency’, 20, …
‘LearnRateSchedule’, ‘piecewise’, …
‘ExecutionEnvironment’, ‘cpu’, …
‘Shuffle’, ‘every-epoch’, …
‘ValidationData’, validDSConverted, … % Correct specification of ValidationData
‘ValidationFrequency’, 50, …
‘ValidationPatience’, 4, …
‘Plots’, ‘training-progress’, …
‘GradientThresholdMethod’, ‘l2norm’, …
‘GradientThreshold’, 0.05);

Error using nnet.cnn.TrainingOptionsMiniBatch
The value of ‘ValidationData’ is invalid. Invalid transform function defined on datastore.

Error in nnet.cnn.TrainingOptionsSGDM (line 128)
this = this@nnet.cnn.TrainingOptionsMiniBatch(args.Definition,varargin{:});

Error in trainingOptions (line 440)
opts = nnet.cnn.TrainingOptionsSGDM(varargin{:});

Caused by:
Attempt to grow array along ambiguous dimension.I am tryeing to train neural network training images.
My code is based on this article: https://blogs.mathworks.com/deep-learning/2021/05/10/semantic-segmentation-for-medical-imaging/#respond

Here is complete code of my script:
% Clear workspace
clear; close all; clc;

% Specify the directory path you want to delete
directoryPath = ‘training_images/resized’;

% Check if the directory exists before trying to delete it
if exist(directoryPath, ‘dir’)
% Remove the directory and its contents
[status, message, messageId] = rmdir(directoryPath, ‘s’);

% Check if the operation was successful
if status
disp([‘Directory "’, directoryPath, ‘" has been deleted successfully.’]);
else
disp([‘Failed to delete "’, directoryPath, ‘": ‘, message]);
end
else
disp([‘Directory "’, directoryPath, ‘" does not exist.’]);
end

% All images
imds = imageDatastore( …
‘training_images’, …
‘IncludeSubfolders’, …
true …
);

% Define class names and their corresponding IDs
classNames = ["Lesion","Background"];
labelIDs =[255,0];

% Create a pixelLabelDatastore holding the ground truth pixel labels
pxds=pixelLabelDatastore( …
‘training_images’, …
classNames, …
labelIDs …
);

% Create a pixel label image datastore of all images
pximds=pixelLabelImageDatastore(imds,pxds);

% Number of Images
total_num_images=length(pximds.Images);

% Visualize random images
perm=randperm(total_num_images,4);
figure;

% Visualize the images with Mask

for idx = 1:length(perm)
% Read the image from imds
image = readimage(imds, perm(idx));

% Read the corresponding mask from pxds
mask = readimage(pxds, perm(idx));

% Display the image
subplot(2,2,idx);
imshow(image);
hold on;

binaryMask = mask(:,:,1) == classNames(1);

% Visualize boundaries on the binary mask
visboundaries(binaryMask, ‘Color’, ‘r’);

% Extract the filename for the title from imds
[~, filename] = fileparts(imds.Files{perm(idx)});
title(sprintf(‘%s’, filename), ‘Interpreter’, "none");
end

% Desired Image Size
imageSize=[224 224 3];

% Create a pixel label image datastore of all resized images

% Specify directories for resized images and labels
resizedImagesDir = ‘training_images/images-segmantation/resized/images’;
resizedLabelsDir = ‘training_images/images-segmantation/resized/labels’;

% Ensure the directories exist
if ~exist(resizedImagesDir, ‘dir’), mkdir(resizedImagesDir); end
if ~exist(resizedLabelsDir, ‘dir’), mkdir(resizedLabelsDir); end

% Resize and save images
for i = 1:length(imds.Files)
img = imread(imds.Files{i});
resizedImg = imresize(img, ‘OutputSize’, imageSize(1:2));
[~, fileName, ext] = fileparts(imds.Files{i});
imwrite(resizedImg, fullfile(resizedImagesDir, [fileName, ext]));
end

% Resize and save labels
for i = 1:length(pxds.Files)
label = imread(pxds.Files{i});
resizedLabel = imresize(label, ‘OutputSize’, imageSize(1:2), ‘Method’, ‘nearest’);
[~, fileName, ext] = fileparts(pxds.Files{i});
imwrite(resizedLabel, fullfile(resizedLabelsDir, [fileName, ext]));
end

% Create new ImageDatastore and PixelLabelDatastore from the resized data
imdsResized = imageDatastore(resizedImagesDir);
pximdsResz = pixelLabelDatastore(resizedLabelsDir, classNames, labelIDs);

% Clear all variables except the necessary variables
% clearvars -except pximdsResz classNames total_num_images imageSize

% Split the dataset into training, validation, and testing sets
numImages = numel(imdsResized.Files);

% Adjust the number as needed
testIdx = randperm(numImages, 5);
trainValidIdx = setdiff(1:numImages, testIdx);

% Adjust the number as needed
validIdx = trainValidIdx(randperm(length(trainValidIdx), 10));
trainIdx = trainValidIdx;

% Create datastores for training, validation, and testing sets
imdsTrain = subset(imdsResized, trainIdx);
imdsValid = subset(imdsResized, validIdx);
imdsTest = subset(imdsResized, testIdx);

pximdsTrain = subset(pximdsResz, trainIdx);
pximdsValid = subset(pximdsResz, validIdx);
pximdsTest = subset(pximdsResz, testIdx);

% Combine the validation image datastore with the pixel label datastore
% ValidationData expects a cell array with these combined datastores
validDS = combine(imdsValid, pximdsValid);

% As of MATLAB R2021a, this might not be necessary, and you can directly use validDS
validationData = {validDS};

% Applying the corrected transformation
trainDSConverted = transform(pximdsTrain, @(c) categoricalToNumeric(c{1}, classNames));
validDSConverted = transform(pximdsValid, @(c) categoricalToNumeric(c{1}, classNames));

% trainDSConverted2 = transform(trainDS, @(data) transformData(data));
% validDSConverted2 = transform(validDS, @(data) transformData(data));

% trainDSConverted3 = transform(trainDS, @transformData2);
% validDSConverted3 = transform(validDS, @transformData2);

% For imageDatastore
imdsTrain = subset(imdsResized, trainIdx);
imdsValid = subset(imdsResized, validIdx);
imdsTest = subset(imdsResized, testIdx);

% For pixelLabelDatastore
pxdsTrain = subset(pximdsResz, trainIdx);
pxdsValid = subset(pximdsResz, validIdx);
pxdsTest = subset(pximdsResz, testIdx);

pximdsTrain = pixelLabelImageDatastore(imdsTrain, pxdsTrain);
pximdsValid = pixelLabelImageDatastore(imdsValid, pxdsValid);

% Specify network architecture
numClasses = numel(classNames);

% Adjust based on your actual image size
imageSize = [224 224 3];

% Example using ResNet-18 backbone
lgraph = deeplabv3plusLayers(imageSize, numClasses, ‘resnet50’);

% Define the parameters for the network
options = trainingOptions(‘sgdm’, …
‘InitialLearnRate’, 0.03, …
‘Momentum’, 0.9, …
‘L2Regularization’, 0.0005, …
‘MaxEpochs’, 20, …
‘MiniBatchSize’, 32, …
‘VerboseFrequency’, 20, …
‘LearnRateSchedule’, ‘piecewise’, …
‘ExecutionEnvironment’, ‘cpu’, …
‘Shuffle’, ‘every-epoch’, …
‘ValidationData’, validDSConverted, … % Correct specification of ValidationData
‘ValidationFrequency’, 50, …
‘ValidationPatience’, 4, …
‘Plots’, ‘training-progress’, …
‘GradientThresholdMethod’, ‘l2norm’, …
‘GradientThreshold’, 0.05);

% Train the network
net = trainNetwork(trainDSConverted, lgraph, options);

% Semantic segmentation of test dataset based on the trained network
[pxdspredicted]=semanticseg(pximdsTest,net,’WriteLocation’,tempdir);

% Evaluation
metrics=evaluateSemanticSegmentation(pxdspredicted,pximdsTest);

% Normalized Confusion Matrix
normConfMatData = metrics.NormalizedConfusionMatrix.Variables;
figure
h=heatmap(classNames,classNames,100*normConfMatData);
h.XLabel=’Predicted Class’;
h.YLabel=’True Class’;
h.Title=’Normalized Confusion Matrix (%)’;

% Number of Images
num_test_images=length(pximdsTest.Images);

% Pick any random 2 images
perm=randperm(num_test_images,2);

% Visualize the images with Mask
for idx=1:length(perm)

% Extract filename for the title
[~,filename]=fileparts(pximdsTest.Images{idx});

% Read the original file and resize it for network purposes
I = imread(pximdsTest.Images{perm(idx)});
I = imresize(I,[imageSize(1) imageSize(2)],’bilinear’);

figure;
image(I);
hold on;

% Read the actual mask and resize it for visualization
actual_mask=imread(pximdsTest.PixelLabelData{perm(idx)});
actual_mask=imresize(actual_mask,[imageSize(1) imageSize(2)],’bilinear’);

% Ground Truth
visboundaries(actual_mask,’Color’,’r’);

% Predicted by the Algorithm
predicted_image=(uint8(readimage(pxdspredicted,perm(idx)))); % Values are 1 and 2
predicted_results=uint8(~(predicted_image-1)); % Conversion to binary and reverse the polarity to match with the labelIds
visboundaries(predicted_results,’Color’,’g’);
title(sprintf(‘%s Red- Actual, Green – Predicted’,filename),’Interpreter’,"none");
imwrite(mat2gray(predicted_results),sprintf(‘%s.png’,filename));

end

% Corrected transformation function

function outImg = categoricalToNumeric(inCategorical, classNames)
outImg = zeros(size(inCategorical,1), size(inCategorical,2), ‘uint8’);
for k = 1:length(classNames)
outImg(inCategorical == classNames{k}) = k-1;
end
end

function [img, label] = transformData(data)
% Assumes data is a cell with {image, label} format
img = data{1}; % Image data
inCategorical = data{2}; % Categorical label data
classNames = ["Lesion","Background"]; % Class names as used previously

outImg = zeros(size(inCategorical,1), size(inCategorical,2), ‘uint8’);
for k = 1:length(classNames)
outImg(inCategorical == classNames(k)) = k-1;
end
label = outImg; % Numeric label data
end

function dataOut = transformData2(dataIn)
img = dataIn{1}; % Assuming dataIn{1} is the image
label = dataIn{2}; % Assuming dataIn{2} is the label in numeric format

% Your conversion logic here, ending with…
labelCategorical = categorical(label, 0:max(label(:)), {‘Background’, ‘Lesion’});

dataOut = {img, labelCategorical};
end

When I run it, I get an error at this part:
% Define the parameters for the network
options = trainingOptions(‘sgdm’, …
‘InitialLearnRate’, 0.03, …
‘Momentum’, 0.9, …
‘L2Regularization’, 0.0005, …
‘MaxEpochs’, 20, …
‘MiniBatchSize’, 32, …
‘VerboseFrequency’, 20, …
‘LearnRateSchedule’, ‘piecewise’, …
‘ExecutionEnvironment’, ‘cpu’, …
‘Shuffle’, ‘every-epoch’, …
‘ValidationData’, validDSConverted, … % Correct specification of ValidationData
‘ValidationFrequency’, 50, …
‘ValidationPatience’, 4, …
‘Plots’, ‘training-progress’, …
‘GradientThresholdMethod’, ‘l2norm’, …
‘GradientThreshold’, 0.05);

Error using nnet.cnn.TrainingOptionsMiniBatch
The value of ‘ValidationData’ is invalid. Invalid transform function defined on datastore.

Error in nnet.cnn.TrainingOptionsSGDM (line 128)
this = this@nnet.cnn.TrainingOptionsMiniBatch(args.Definition,varargin{:});

Error in trainingOptions (line 440)
opts = nnet.cnn.TrainingOptionsSGDM(varargin{:});

Caused by:
Attempt to grow array along ambiguous dimension. I am tryeing to train neural network training images.
My code is based on this article: https://blogs.mathworks.com/deep-learning/2021/05/10/semantic-segmentation-for-medical-imaging/#respond

Here is complete code of my script:
% Clear workspace
clear; close all; clc;

% Specify the directory path you want to delete
directoryPath = ‘training_images/resized’;

% Check if the directory exists before trying to delete it
if exist(directoryPath, ‘dir’)
% Remove the directory and its contents
[status, message, messageId] = rmdir(directoryPath, ‘s’);

% Check if the operation was successful
if status
disp([‘Directory "’, directoryPath, ‘" has been deleted successfully.’]);
else
disp([‘Failed to delete "’, directoryPath, ‘": ‘, message]);
end
else
disp([‘Directory "’, directoryPath, ‘" does not exist.’]);
end

% All images
imds = imageDatastore( …
‘training_images’, …
‘IncludeSubfolders’, …
true …
);

% Define class names and their corresponding IDs
classNames = ["Lesion","Background"];
labelIDs =[255,0];

% Create a pixelLabelDatastore holding the ground truth pixel labels
pxds=pixelLabelDatastore( …
‘training_images’, …
classNames, …
labelIDs …
);

% Create a pixel label image datastore of all images
pximds=pixelLabelImageDatastore(imds,pxds);

% Number of Images
total_num_images=length(pximds.Images);

% Visualize random images
perm=randperm(total_num_images,4);
figure;

% Visualize the images with Mask

for idx = 1:length(perm)
% Read the image from imds
image = readimage(imds, perm(idx));

% Read the corresponding mask from pxds
mask = readimage(pxds, perm(idx));

% Display the image
subplot(2,2,idx);
imshow(image);
hold on;

binaryMask = mask(:,:,1) == classNames(1);

% Visualize boundaries on the binary mask
visboundaries(binaryMask, ‘Color’, ‘r’);

% Extract the filename for the title from imds
[~, filename] = fileparts(imds.Files{perm(idx)});
title(sprintf(‘%s’, filename), ‘Interpreter’, "none");
end

% Desired Image Size
imageSize=[224 224 3];

% Create a pixel label image datastore of all resized images

% Specify directories for resized images and labels
resizedImagesDir = ‘training_images/images-segmantation/resized/images’;
resizedLabelsDir = ‘training_images/images-segmantation/resized/labels’;

% Ensure the directories exist
if ~exist(resizedImagesDir, ‘dir’), mkdir(resizedImagesDir); end
if ~exist(resizedLabelsDir, ‘dir’), mkdir(resizedLabelsDir); end

% Resize and save images
for i = 1:length(imds.Files)
img = imread(imds.Files{i});
resizedImg = imresize(img, ‘OutputSize’, imageSize(1:2));
[~, fileName, ext] = fileparts(imds.Files{i});
imwrite(resizedImg, fullfile(resizedImagesDir, [fileName, ext]));
end

% Resize and save labels
for i = 1:length(pxds.Files)
label = imread(pxds.Files{i});
resizedLabel = imresize(label, ‘OutputSize’, imageSize(1:2), ‘Method’, ‘nearest’);
[~, fileName, ext] = fileparts(pxds.Files{i});
imwrite(resizedLabel, fullfile(resizedLabelsDir, [fileName, ext]));
end

% Create new ImageDatastore and PixelLabelDatastore from the resized data
imdsResized = imageDatastore(resizedImagesDir);
pximdsResz = pixelLabelDatastore(resizedLabelsDir, classNames, labelIDs);

% Clear all variables except the necessary variables
% clearvars -except pximdsResz classNames total_num_images imageSize

% Split the dataset into training, validation, and testing sets
numImages = numel(imdsResized.Files);

% Adjust the number as needed
testIdx = randperm(numImages, 5);
trainValidIdx = setdiff(1:numImages, testIdx);

% Adjust the number as needed
validIdx = trainValidIdx(randperm(length(trainValidIdx), 10));
trainIdx = trainValidIdx;

% Create datastores for training, validation, and testing sets
imdsTrain = subset(imdsResized, trainIdx);
imdsValid = subset(imdsResized, validIdx);
imdsTest = subset(imdsResized, testIdx);

pximdsTrain = subset(pximdsResz, trainIdx);
pximdsValid = subset(pximdsResz, validIdx);
pximdsTest = subset(pximdsResz, testIdx);

% Combine the validation image datastore with the pixel label datastore
% ValidationData expects a cell array with these combined datastores
validDS = combine(imdsValid, pximdsValid);

% As of MATLAB R2021a, this might not be necessary, and you can directly use validDS
validationData = {validDS};

% Applying the corrected transformation
trainDSConverted = transform(pximdsTrain, @(c) categoricalToNumeric(c{1}, classNames));
validDSConverted = transform(pximdsValid, @(c) categoricalToNumeric(c{1}, classNames));

% trainDSConverted2 = transform(trainDS, @(data) transformData(data));
% validDSConverted2 = transform(validDS, @(data) transformData(data));

% trainDSConverted3 = transform(trainDS, @transformData2);
% validDSConverted3 = transform(validDS, @transformData2);

% For imageDatastore
imdsTrain = subset(imdsResized, trainIdx);
imdsValid = subset(imdsResized, validIdx);
imdsTest = subset(imdsResized, testIdx);

% For pixelLabelDatastore
pxdsTrain = subset(pximdsResz, trainIdx);
pxdsValid = subset(pximdsResz, validIdx);
pxdsTest = subset(pximdsResz, testIdx);

pximdsTrain = pixelLabelImageDatastore(imdsTrain, pxdsTrain);
pximdsValid = pixelLabelImageDatastore(imdsValid, pxdsValid);

% Specify network architecture
numClasses = numel(classNames);

% Adjust based on your actual image size
imageSize = [224 224 3];

% Example using ResNet-18 backbone
lgraph = deeplabv3plusLayers(imageSize, numClasses, ‘resnet50’);

% Define the parameters for the network
options = trainingOptions(‘sgdm’, …
‘InitialLearnRate’, 0.03, …
‘Momentum’, 0.9, …
‘L2Regularization’, 0.0005, …
‘MaxEpochs’, 20, …
‘MiniBatchSize’, 32, …
‘VerboseFrequency’, 20, …
‘LearnRateSchedule’, ‘piecewise’, …
‘ExecutionEnvironment’, ‘cpu’, …
‘Shuffle’, ‘every-epoch’, …
‘ValidationData’, validDSConverted, … % Correct specification of ValidationData
‘ValidationFrequency’, 50, …
‘ValidationPatience’, 4, …
‘Plots’, ‘training-progress’, …
‘GradientThresholdMethod’, ‘l2norm’, …
‘GradientThreshold’, 0.05);

% Train the network
net = trainNetwork(trainDSConverted, lgraph, options);

% Semantic segmentation of test dataset based on the trained network
[pxdspredicted]=semanticseg(pximdsTest,net,’WriteLocation’,tempdir);

% Evaluation
metrics=evaluateSemanticSegmentation(pxdspredicted,pximdsTest);

% Normalized Confusion Matrix
normConfMatData = metrics.NormalizedConfusionMatrix.Variables;
figure
h=heatmap(classNames,classNames,100*normConfMatData);
h.XLabel=’Predicted Class’;
h.YLabel=’True Class’;
h.Title=’Normalized Confusion Matrix (%)’;

% Number of Images
num_test_images=length(pximdsTest.Images);

% Pick any random 2 images
perm=randperm(num_test_images,2);

% Visualize the images with Mask
for idx=1:length(perm)

% Extract filename for the title
[~,filename]=fileparts(pximdsTest.Images{idx});

% Read the original file and resize it for network purposes
I = imread(pximdsTest.Images{perm(idx)});
I = imresize(I,[imageSize(1) imageSize(2)],’bilinear’);

figure;
image(I);
hold on;

% Read the actual mask and resize it for visualization
actual_mask=imread(pximdsTest.PixelLabelData{perm(idx)});
actual_mask=imresize(actual_mask,[imageSize(1) imageSize(2)],’bilinear’);

% Ground Truth
visboundaries(actual_mask,’Color’,’r’);

% Predicted by the Algorithm
predicted_image=(uint8(readimage(pxdspredicted,perm(idx)))); % Values are 1 and 2
predicted_results=uint8(~(predicted_image-1)); % Conversion to binary and reverse the polarity to match with the labelIds
visboundaries(predicted_results,’Color’,’g’);
title(sprintf(‘%s Red- Actual, Green – Predicted’,filename),’Interpreter’,"none");
imwrite(mat2gray(predicted_results),sprintf(‘%s.png’,filename));

end

% Corrected transformation function

function outImg = categoricalToNumeric(inCategorical, classNames)
outImg = zeros(size(inCategorical,1), size(inCategorical,2), ‘uint8’);
for k = 1:length(classNames)
outImg(inCategorical == classNames{k}) = k-1;
end
end

function [img, label] = transformData(data)
% Assumes data is a cell with {image, label} format
img = data{1}; % Image data
inCategorical = data{2}; % Categorical label data
classNames = ["Lesion","Background"]; % Class names as used previously

outImg = zeros(size(inCategorical,1), size(inCategorical,2), ‘uint8’);
for k = 1:length(classNames)
outImg(inCategorical == classNames(k)) = k-1;
end
label = outImg; % Numeric label data
end

function dataOut = transformData2(dataIn)
img = dataIn{1}; % Assuming dataIn{1} is the image
label = dataIn{2}; % Assuming dataIn{2} is the label in numeric format

% Your conversion logic here, ending with…
labelCategorical = categorical(label, 0:max(label(:)), {‘Background’, ‘Lesion’});

dataOut = {img, labelCategorical};
end

When I run it, I get an error at this part:
% Define the parameters for the network
options = trainingOptions(‘sgdm’, …
‘InitialLearnRate’, 0.03, …
‘Momentum’, 0.9, …
‘L2Regularization’, 0.0005, …
‘MaxEpochs’, 20, …
‘MiniBatchSize’, 32, …
‘VerboseFrequency’, 20, …
‘LearnRateSchedule’, ‘piecewise’, …
‘ExecutionEnvironment’, ‘cpu’, …
‘Shuffle’, ‘every-epoch’, …
‘ValidationData’, validDSConverted, … % Correct specification of ValidationData
‘ValidationFrequency’, 50, …
‘ValidationPatience’, 4, …
‘Plots’, ‘training-progress’, …
‘GradientThresholdMethod’, ‘l2norm’, …
‘GradientThreshold’, 0.05);

Error using nnet.cnn.TrainingOptionsMiniBatch
The value of ‘ValidationData’ is invalid. Invalid transform function defined on datastore.

Error in nnet.cnn.TrainingOptionsSGDM (line 128)
this = this@nnet.cnn.TrainingOptionsMiniBatch(args.Definition,varargin{:});

Error in trainingOptions (line 440)
opts = nnet.cnn.TrainingOptionsSGDM(varargin{:});

Caused by:
Attempt to grow array along ambiguous dimension. trainnetwork, trainingoptions MATLAB Answers — New Questions

​

Hello,
I am trying to connect to a Raspberry Pi 4-B with "MATLAB Support Package for Raspberry Pi Hardware". I have selected "Costomized the existing Rasbian OS running on my hardware". I wrote this code after the installation was complete: r = raspi(‘192.168.0.99′,’pi’,’raspberry’) where 192.168.0.99 is the IP of the board connecting via WIFI. After a while, I encountered this error: Error executing command "make ONLY_MATLAB_IO=1 -C /opt/MATLAB/mw_server_v22.1.0 -f Makefile".
Can you help me what to do?
The complete response of the MATLAB in command window is as follows:
myrasp = raspi(‘192.168.0.99′,’pi’,’raspberry’)
### Updating Raspberry Pi I/O server…
### Connecting to board…
### Connected to 192.168.0.99…
### Installing userland software (this might take a while)…
### Installing nanomsg Library (this might take a while)…
### Creating server folder…
### Transferring source files…
### Building MATLAB I/O server…
Error executing command "make ONLY_MATLAB_IO=1 -C /opt/MATLAB/mw_server_v22.1.0 -f Makefile". Details:

STDERR: IO_wrapperv4l2.c: In function ‘v4l2CameraSettings’:
IO_wrapperv4l2.c:133:14: warning: pointer targets in assignment from ‘uint8_T *’ {aka ‘unsigned char *’} to ‘char_T *’ {aka ‘char
*’} differ in signedness [-Wpointer-sign]
133 | jsonData = &payloadBufferRx[index];
| ^
IO_wrapperCameraboard.c: In function ‘camerboardInitialize’:
IO_wrapperCameraboard.c:38:19: warning: pointer targets in assignment from ‘uint8_T *’ {aka ‘unsigned char *’} to ‘char_T *’ {aka
‘char *’} differ in signedness [-Wpointer-sign]
38 | controlParams = &payloadBufferRx[index];
| ^
IO_wrapperCameraboard.c:41:5: warning: implicit declaration of function ‘printf’ [-Wimplicit-function-declaration]
41 | printf("REQUEST_CAMERABOARD_INIT: (w, h, fps, q) = [%d, %d, %d, %d]n", width, height,
| ^~~~~~
IO_wrapperCameraboard.c:41:5: warning: incompatible implicit declaration of built-in function ‘printf’
IO_wrapperCameraboard.c:11:1: note: include ‘<stdio.h>’ or provide a declaration of ‘printf’
10 | #include "IO_wrapperCameraboard.h"
+++ |+#include <stdio.h>
11 |
IO_wrapperCameraboard.c: In function ‘cameraboardSnapshot’:
IO_wrapperCameraboard.c:66:5: warning: incompatible implicit declaration of built-in function ‘printf’
66 | printf("REQUEST_CAMERABOARD_SNAPSHOT n");
| ^~~~~~
IO_wrapperCameraboard.c:66:5: note: include ‘<stdio.h>’ or provide a declaration of ‘printf’
IO_wrapperCameraboard.c: In function ‘cameraboardControl’:
IO_wrapperCameraboard.c:95:19: warning: pointer targets in assignment from ‘uint8_T *’ {aka ‘unsigned char *’} to ‘char_T *’ {aka
‘char *’} differ in signedness [-Wpointer-sign]
95 | controlParams = &payloadBufferRx[0];
| ^
IO_wrapperCameraboard.c:96:5: warning: incompatible implicit declaration of built-in function ‘printf’
96 | printf("REQUEST_CAMERABOARD_CONTROL: %s n", controlParams);
| ^~~~~~
IO_wrapperCameraboard.c:96:5: note: include ‘<stdio.h>’ or provide a declaration of ‘printf’
IO_wrapperCameraboard.c: In function ‘cameraboardTerminate’:
IO_wrapperCameraboard.c:118:5: warning: incompatible implicit declaration of built-in function ‘printf’
118 | printf("REQUEST_CAMERABOARD_TERMINATE n");
| ^~~~~~
IO_wrapperCameraboard.c:118:5: note: include ‘<stdio.h>’ or provide a declaration of ‘printf’
In file included from sharedServer.h:16,
from mw_wrapperCANChannel.h:16,
from mw_wrapperCANChannel.c:11:
mw_wrapperCANChannel.c: In function ‘canWrite’:
mw_wrapperCANChannel.c:18:23: warning: too many arguments for format [-Wformat-extra-args]
18 | LOG_PRINT(stdout, "REQUEST_CAN_WRITE: n",0);
| ^~~~~~~~~~~~~~~~~~~~~~~
common.h:12:51: note: in definition of macro ‘LOG_PRINT’
12 | #define LOG_PRINT(fd, format, …) fprintf(fd, format, __VA_ARGS__); fflush(fd)
| ^~~~~~
mw_wrapperCANChannel.c:53:26: warning: too many arguments for format [-Wformat-extra-args]
53 | LOG_PRINT(stdout,"CANChannel: Write successful n",0);
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
common.h:12:51: note: in definition of macro ‘LOG_PRINT’
12 | #define LOG_PRINT(fd, format, …) fprintf(fd, format, __VA_ARGS__); fflush(fd)
| ^~~~~~
mw_wrapperCANChannel.c:56:26: warning: too many arguments for format [-Wformat-extra-args]
56 | LOG_PRINT(stdout,"CANChannel: Write failed n",0);
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~
common.h:12:51: note: in definition of macro ‘LOG_PRINT’
12 | #define LOG_PRINT(fd, format, …) fprintf(fd, format, __VA_ARGS__); fflush(fd)
| ^~~~~~
mw_wrapperCANChannel.c: In function ‘canRead’:
mw_wrapperCANChannel.c:65:22: warning: too many arguments for format [-Wformat-extra-args]
65 | LOG_PRINT(stdout,"REQUEST_CAN_Read: n",0);
| ^~~~~~~~~~~~~~~~~~~~~~
common.h:12:51: note: in definition of macro ‘LOG_PRINT’
12 | #define LOG_PRINT(fd, format, …) fprintf(fd, format, __VA_ARGS__); fflush(fd)
| ^~~~~~
mw_wrapperCANChannel.c:98:26: warning: too many arguments for format [-Wformat-extra-args]
98 | LOG_PRINT(stdout,"CANChannel: Read successful n",0);
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
common.h:12:51: note: in definition of macro ‘LOG_PRINT’
12 | #define LOG_PRINT(fd, format, …) fprintf(fd, format, __VA_ARGS__); fflush(fd)
| ^~~~~~
mw_wrapperCANChannel.c:100:26: warning: too many arguments for format [-Wformat-extra-args]
100 | LOG_PRINT(stdout,"CANChannel: Read failed n",0);
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~
common.h:12:51: note: in definition of macro ‘LOG_PRINT’
12 | #define LOG_PRINT(fd, format, …) fprintf(fd, format, __VA_ARGS__); fflush(fd)
| ^~~~~~
picam.c:19:10: fatal error: RaspiCamControl.h: No such file or directory
19 | #include "RaspiCamControl.h"
| ^~~~~~~~~~~~~~~~~~~
compilation terminated.
make: *** [Makefile:81: obj/picam.o] Error 1

STDOUT: make: Entering directory ‘/opt/MATLAB/mw_server_v22.1.0’
[Compiling] IO_wrapperv4l2.c
gcc -c -g -O0 -D_DEBUG -Wall -I/opt/userland -I/opt/userland/host_applications/linux/libs/bcm_host/include
-I/opt/userland/interface/vcos -I/opt/userland/interface/vcos/pthreads -I/opt/userland/interface/vmcs_host/linux
-I/opt/userland/host_applications/linux/apps/raspicam -I/opt/nanomsg/include -Winline -pipe -D_DEBUG -D_MATLABIO_ IO_wrapperv4l2.c
-o obj/IO_wrapperv4l2.o
[Compiling] MW_PWM.c
gcc -c -g -O0 -D_DEBUG -Wall -I/opt/userland -I/opt/userland/host_applications/linux/libs/bcm_host/include
-I/opt/userland/interface/vcos -I/opt/userland/interface/vcos/pthreads -I/opt/userland/interface/vmcs_host/linux
-I/opt/userland/host_applications/linux/apps/raspicam -I/opt/nanomsg/include -Winline -pipe -D_DEBUG -D_MATLABIO_ MW_PWM.c -o
obj/MW_PWM.o
[Compiling] MW_pigs.c
gcc -c -g -O0 -D_DEBUG -Wall -I/opt/userland -I/opt/userland/host_applications/linux/libs/bcm_host/include
-I/opt/userland/interface/vcos -I/opt/userland/interface/vcos/pthreads -I/opt/userland/interface/vmcs_host/linux
-I/opt/userland/host_applications/linux/apps/raspicam -I/opt/nanomsg/include -Winline -pipe -D_DEBUG -D_MATLABIO_ MW_pigs.c -o
obj/MW_pigs.o
[Compiling] sharedServer.c
gcc -c -g -O0 -D_DEBUG -Wall -I/opt/userland -I/opt/userland/host_applications/linux/libs/bcm_host/include
-I/opt/userland/interface/vcos -I/opt/userland/interface/vcos/pthreads -I/opt/userland/interface/vmcs_host/linux
-I/opt/userland/host_applications/linux/apps/raspicam -I/opt/nanomsg/include -Winline -pipe -D_DEBUG -D_MATLABIO_ sharedServer.c
-o obj/sharedServer.o
[Compiling] IO_wrapperCameraboard.c
gcc -c -g -O0 -D_DEBUG -Wall -I/opt/userland -I/opt/userland/host_applications/linux/libs/bcm_host/include
-I/opt/userland/interface/vcos -I/opt/userland/interface/vcos/pthreads -I/opt/userland/interface/vmcs_host/linux
-I/opt/userland/host_applications/linux/apps/raspicam -I/opt/nanomsg/include -Winline -pipe -D_DEBUG -D_MATLABIO_
IO_wrapperCameraboard.c -o obj/IO_wrapperCameraboard.o
[Compiling] mw_wrapperCANChannel.c
gcc -c -g -O0 -D_DEBUG -Wall -I/opt/userland -I/opt/userland/host_applications/linux/libs/bcm_host/include
-I/opt/userland/interface/vcos -I/opt/userland/interface/vcos/pthreads -I/opt/userland/interface/vmcs_host/linux
-I/opt/userland/host_applications/linux/apps/raspicam -I/opt/nanomsg/include -Winline -pipe -D_DEBUG -D_MATLABIO_
mw_wrapperCANChannel.c -o obj/mw_wrapperCANChannel.o
[Compiling] LED.c
gcc -c -g -O0 -D_DEBUG -Wall -I/opt/userland -I/opt/userland/host_applications/linux/libs/bcm_host/include
-I/opt/userland/interface/vcos -I/opt/userland/interface/vcos/pthreads -I/opt/userland/interface/vmcs_host/linux
-I/opt/userland/host_applications/linux/apps/raspicam -I/opt/nanomsg/include -Winline -pipe -D_DEBUG -D_MATLABIO_ LED.c -o
obj/LED.o
[Compiling] picam.c
gcc -c -g -O0 -D_DEBUG -Wall -I/opt/userland -I/opt/userland/host_applications/linux/libs/bcm_host/include
-I/opt/userland/interface/vcos -I/opt/userland/interface/vcos/pthreads -I/opt/userland/interface/vmcs_host/linux
-I/opt/userland/host_applications/linux/apps/raspicam -I/opt/nanomsg/include -Winline -pipe -D_DEBUG -D_MATLABIO_ picam.c -o
obj/picam.o
make: Leaving directory ‘/opt/MATLAB/mw_server_v22.1.0’Hello,
I am trying to connect to a Raspberry Pi 4-B with "MATLAB Support Package for Raspberry Pi Hardware". I have selected "Costomized the existing Rasbian OS running on my hardware". I wrote this code after the installation was complete: r = raspi(‘192.168.0.99′,’pi’,’raspberry’) where 192.168.0.99 is the IP of the board connecting via WIFI. After a while, I encountered this error: Error executing command "make ONLY_MATLAB_IO=1 -C /opt/MATLAB/mw_server_v22.1.0 -f Makefile".
Can you help me what to do?
The complete response of the MATLAB in command window is as follows:
myrasp = raspi(‘192.168.0.99′,’pi’,’raspberry’)
### Updating Raspberry Pi I/O server…
### Connecting to board…
### Connected to 192.168.0.99…
### Installing userland software (this might take a while)…
### Installing nanomsg Library (this might take a while)…
### Creating server folder…
### Transferring source files…
### Building MATLAB I/O server…
Error executing command "make ONLY_MATLAB_IO=1 -C /opt/MATLAB/mw_server_v22.1.0 -f Makefile". Details:

STDERR: IO_wrapperv4l2.c: In function ‘v4l2CameraSettings’:
IO_wrapperv4l2.c:133:14: warning: pointer targets in assignment from ‘uint8_T *’ {aka ‘unsigned char *’} to ‘char_T *’ {aka ‘char
*’} differ in signedness [-Wpointer-sign]
133 | jsonData = &payloadBufferRx[index];
| ^
IO_wrapperCameraboard.c: In function ‘camerboardInitialize’:
IO_wrapperCameraboard.c:38:19: warning: pointer targets in assignment from ‘uint8_T *’ {aka ‘unsigned char *’} to ‘char_T *’ {aka
‘char *’} differ in signedness [-Wpointer-sign]
38 | controlParams = &payloadBufferRx[index];
| ^
IO_wrapperCameraboard.c:41:5: warning: implicit declaration of function ‘printf’ [-Wimplicit-function-declaration]
41 | printf("REQUEST_CAMERABOARD_INIT: (w, h, fps, q) = [%d, %d, %d, %d]n", width, height,
| ^~~~~~
IO_wrapperCameraboard.c:41:5: warning: incompatible implicit declaration of built-in function ‘printf’
IO_wrapperCameraboard.c:11:1: note: include ‘<stdio.h>’ or provide a declaration of ‘printf’
10 | #include "IO_wrapperCameraboard.h"
+++ |+#include <stdio.h>
11 |
IO_wrapperCameraboard.c: In function ‘cameraboardSnapshot’:
IO_wrapperCameraboard.c:66:5: warning: incompatible implicit declaration of built-in function ‘printf’
66 | printf("REQUEST_CAMERABOARD_SNAPSHOT n");
| ^~~~~~
IO_wrapperCameraboard.c:66:5: note: include ‘<stdio.h>’ or provide a declaration of ‘printf’
IO_wrapperCameraboard.c: In function ‘cameraboardControl’:
IO_wrapperCameraboard.c:95:19: warning: pointer targets in assignment from ‘uint8_T *’ {aka ‘unsigned char *’} to ‘char_T *’ {aka
‘char *’} differ in signedness [-Wpointer-sign]
95 | controlParams = &payloadBufferRx[0];
| ^
IO_wrapperCameraboard.c:96:5: warning: incompatible implicit declaration of built-in function ‘printf’
96 | printf("REQUEST_CAMERABOARD_CONTROL: %s n", controlParams);
| ^~~~~~
IO_wrapperCameraboard.c:96:5: note: include ‘<stdio.h>’ or provide a declaration of ‘printf’
IO_wrapperCameraboard.c: In function ‘cameraboardTerminate’:
IO_wrapperCameraboard.c:118:5: warning: incompatible implicit declaration of built-in function ‘printf’
118 | printf("REQUEST_CAMERABOARD_TERMINATE n");
| ^~~~~~
IO_wrapperCameraboard.c:118:5: note: include ‘<stdio.h>’ or provide a declaration of ‘printf’
In file included from sharedServer.h:16,
from mw_wrapperCANChannel.h:16,
from mw_wrapperCANChannel.c:11:
mw_wrapperCANChannel.c: In function ‘canWrite’:
mw_wrapperCANChannel.c:18:23: warning: too many arguments for format [-Wformat-extra-args]
18 | LOG_PRINT(stdout, "REQUEST_CAN_WRITE: n",0);
| ^~~~~~~~~~~~~~~~~~~~~~~
common.h:12:51: note: in definition of macro ‘LOG_PRINT’
12 | #define LOG_PRINT(fd, format, …) fprintf(fd, format, __VA_ARGS__); fflush(fd)
| ^~~~~~
mw_wrapperCANChannel.c:53:26: warning: too many arguments for format [-Wformat-extra-args]
53 | LOG_PRINT(stdout,"CANChannel: Write successful n",0);
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
common.h:12:51: note: in definition of macro ‘LOG_PRINT’
12 | #define LOG_PRINT(fd, format, …) fprintf(fd, format, __VA_ARGS__); fflush(fd)
| ^~~~~~
mw_wrapperCANChannel.c:56:26: warning: too many arguments for format [-Wformat-extra-args]
56 | LOG_PRINT(stdout,"CANChannel: Write failed n",0);
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~
common.h:12:51: note: in definition of macro ‘LOG_PRINT’
12 | #define LOG_PRINT(fd, format, …) fprintf(fd, format, __VA_ARGS__); fflush(fd)
| ^~~~~~
mw_wrapperCANChannel.c: In function ‘canRead’:
mw_wrapperCANChannel.c:65:22: warning: too many arguments for format [-Wformat-extra-args]
65 | LOG_PRINT(stdout,"REQUEST_CAN_Read: n",0);
| ^~~~~~~~~~~~~~~~~~~~~~
common.h:12:51: note: in definition of macro ‘LOG_PRINT’
12 | #define LOG_PRINT(fd, format, …) fprintf(fd, format, __VA_ARGS__); fflush(fd)
| ^~~~~~
mw_wrapperCANChannel.c:98:26: warning: too many arguments for format [-Wformat-extra-args]
98 | LOG_PRINT(stdout,"CANChannel: Read successful n",0);
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
common.h:12:51: note: in definition of macro ‘LOG_PRINT’
12 | #define LOG_PRINT(fd, format, …) fprintf(fd, format, __VA_ARGS__); fflush(fd)
| ^~~~~~
mw_wrapperCANChannel.c:100:26: warning: too many arguments for format [-Wformat-extra-args]
100 | LOG_PRINT(stdout,"CANChannel: Read failed n",0);
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~
common.h:12:51: note: in definition of macro ‘LOG_PRINT’
12 | #define LOG_PRINT(fd, format, …) fprintf(fd, format, __VA_ARGS__); fflush(fd)
| ^~~~~~
picam.c:19:10: fatal error: RaspiCamControl.h: No such file or directory
19 | #include "RaspiCamControl.h"
| ^~~~~~~~~~~~~~~~~~~
compilation terminated.
make: *** [Makefile:81: obj/picam.o] Error 1

STDOUT: make: Entering directory ‘/opt/MATLAB/mw_server_v22.1.0’
[Compiling] IO_wrapperv4l2.c
gcc -c -g -O0 -D_DEBUG -Wall -I/opt/userland -I/opt/userland/host_applications/linux/libs/bcm_host/include
-I/opt/userland/interface/vcos -I/opt/userland/interface/vcos/pthreads -I/opt/userland/interface/vmcs_host/linux
-I/opt/userland/host_applications/linux/apps/raspicam -I/opt/nanomsg/include -Winline -pipe -D_DEBUG -D_MATLABIO_ IO_wrapperv4l2.c
-o obj/IO_wrapperv4l2.o
[Compiling] MW_PWM.c
gcc -c -g -O0 -D_DEBUG -Wall -I/opt/userland -I/opt/userland/host_applications/linux/libs/bcm_host/include
-I/opt/userland/interface/vcos -I/opt/userland/interface/vcos/pthreads -I/opt/userland/interface/vmcs_host/linux
-I/opt/userland/host_applications/linux/apps/raspicam -I/opt/nanomsg/include -Winline -pipe -D_DEBUG -D_MATLABIO_ MW_PWM.c -o
obj/MW_PWM.o
[Compiling] MW_pigs.c
gcc -c -g -O0 -D_DEBUG -Wall -I/opt/userland -I/opt/userland/host_applications/linux/libs/bcm_host/include
-I/opt/userland/interface/vcos -I/opt/userland/interface/vcos/pthreads -I/opt/userland/interface/vmcs_host/linux
-I/opt/userland/host_applications/linux/apps/raspicam -I/opt/nanomsg/include -Winline -pipe -D_DEBUG -D_MATLABIO_ MW_pigs.c -o
obj/MW_pigs.o
[Compiling] sharedServer.c
gcc -c -g -O0 -D_DEBUG -Wall -I/opt/userland -I/opt/userland/host_applications/linux/libs/bcm_host/include
-I/opt/userland/interface/vcos -I/opt/userland/interface/vcos/pthreads -I/opt/userland/interface/vmcs_host/linux
-I/opt/userland/host_applications/linux/apps/raspicam -I/opt/nanomsg/include -Winline -pipe -D_DEBUG -D_MATLABIO_ sharedServer.c
-o obj/sharedServer.o
[Compiling] IO_wrapperCameraboard.c
gcc -c -g -O0 -D_DEBUG -Wall -I/opt/userland -I/opt/userland/host_applications/linux/libs/bcm_host/include
-I/opt/userland/interface/vcos -I/opt/userland/interface/vcos/pthreads -I/opt/userland/interface/vmcs_host/linux
-I/opt/userland/host_applications/linux/apps/raspicam -I/opt/nanomsg/include -Winline -pipe -D_DEBUG -D_MATLABIO_
IO_wrapperCameraboard.c -o obj/IO_wrapperCameraboard.o
[Compiling] mw_wrapperCANChannel.c
gcc -c -g -O0 -D_DEBUG -Wall -I/opt/userland -I/opt/userland/host_applications/linux/libs/bcm_host/include
-I/opt/userland/interface/vcos -I/opt/userland/interface/vcos/pthreads -I/opt/userland/interface/vmcs_host/linux
-I/opt/userland/host_applications/linux/apps/raspicam -I/opt/nanomsg/include -Winline -pipe -D_DEBUG -D_MATLABIO_
mw_wrapperCANChannel.c -o obj/mw_wrapperCANChannel.o
[Compiling] LED.c
gcc -c -g -O0 -D_DEBUG -Wall -I/opt/userland -I/opt/userland/host_applications/linux/libs/bcm_host/include
-I/opt/userland/interface/vcos -I/opt/userland/interface/vcos/pthreads -I/opt/userland/interface/vmcs_host/linux
-I/opt/userland/host_applications/linux/apps/raspicam -I/opt/nanomsg/include -Winline -pipe -D_DEBUG -D_MATLABIO_ LED.c -o
obj/LED.o
[Compiling] picam.c
gcc -c -g -O0 -D_DEBUG -Wall -I/opt/userland -I/opt/userland/host_applications/linux/libs/bcm_host/include
-I/opt/userland/interface/vcos -I/opt/userland/interface/vcos/pthreads -I/opt/userland/interface/vmcs_host/linux
-I/opt/userland/host_applications/linux/apps/raspicam -I/opt/nanomsg/include -Winline -pipe -D_DEBUG -D_MATLABIO_ picam.c -o
obj/picam.o
make: Leaving directory ‘/opt/MATLAB/mw_server_v22.1.0’ Hello,
I am trying to connect to a Raspberry Pi 4-B with "MATLAB Support Package for Raspberry Pi Hardware". I have selected "Costomized the existing Rasbian OS running on my hardware". I wrote this code after the installation was complete: r = raspi(‘192.168.0.99′,’pi’,’raspberry’) where 192.168.0.99 is the IP of the board connecting via WIFI. After a while, I encountered this error: Error executing command "make ONLY_MATLAB_IO=1 -C /opt/MATLAB/mw_server_v22.1.0 -f Makefile".
Can you help me what to do?
The complete response of the MATLAB in command window is as follows:
myrasp = raspi(‘192.168.0.99′,’pi’,’raspberry’)
### Updating Raspberry Pi I/O server…
### Connecting to board…
### Connected to 192.168.0.99…
### Installing userland software (this might take a while)…
### Installing nanomsg Library (this might take a while)…
### Creating server folder…
### Transferring source files…
### Building MATLAB I/O server…
Error executing command "make ONLY_MATLAB_IO=1 -C /opt/MATLAB/mw_server_v22.1.0 -f Makefile". Details:

STDERR: IO_wrapperv4l2.c: In function ‘v4l2CameraSettings’:
IO_wrapperv4l2.c:133:14: warning: pointer targets in assignment from ‘uint8_T *’ {aka ‘unsigned char *’} to ‘char_T *’ {aka ‘char
*’} differ in signedness [-Wpointer-sign]
133 | jsonData = &payloadBufferRx[index];
| ^
IO_wrapperCameraboard.c: In function ‘camerboardInitialize’:
IO_wrapperCameraboard.c:38:19: warning: pointer targets in assignment from ‘uint8_T *’ {aka ‘unsigned char *’} to ‘char_T *’ {aka
‘char *’} differ in signedness [-Wpointer-sign]
38 | controlParams = &payloadBufferRx[index];
| ^
IO_wrapperCameraboard.c:41:5: warning: implicit declaration of function ‘printf’ [-Wimplicit-function-declaration]
41 | printf("REQUEST_CAMERABOARD_INIT: (w, h, fps, q) = [%d, %d, %d, %d]n", width, height,
| ^~~~~~
IO_wrapperCameraboard.c:41:5: warning: incompatible implicit declaration of built-in function ‘printf’
IO_wrapperCameraboard.c:11:1: note: include ‘<stdio.h>’ or provide a declaration of ‘printf’
10 | #include "IO_wrapperCameraboard.h"
+++ |+#include <stdio.h>
11 |
IO_wrapperCameraboard.c: In function ‘cameraboardSnapshot’:
IO_wrapperCameraboard.c:66:5: warning: incompatible implicit declaration of built-in function ‘printf’
66 | printf("REQUEST_CAMERABOARD_SNAPSHOT n");
| ^~~~~~
IO_wrapperCameraboard.c:66:5: note: include ‘<stdio.h>’ or provide a declaration of ‘printf’
IO_wrapperCameraboard.c: In function ‘cameraboardControl’:
IO_wrapperCameraboard.c:95:19: warning: pointer targets in assignment from ‘uint8_T *’ {aka ‘unsigned char *’} to ‘char_T *’ {aka
‘char *’} differ in signedness [-Wpointer-sign]
95 | controlParams = &payloadBufferRx[0];
| ^
IO_wrapperCameraboard.c:96:5: warning: incompatible implicit declaration of built-in function ‘printf’
96 | printf("REQUEST_CAMERABOARD_CONTROL: %s n", controlParams);
| ^~~~~~
IO_wrapperCameraboard.c:96:5: note: include ‘<stdio.h>’ or provide a declaration of ‘printf’
IO_wrapperCameraboard.c: In function ‘cameraboardTerminate’:
IO_wrapperCameraboard.c:118:5: warning: incompatible implicit declaration of built-in function ‘printf’
118 | printf("REQUEST_CAMERABOARD_TERMINATE n");
| ^~~~~~
IO_wrapperCameraboard.c:118:5: note: include ‘<stdio.h>’ or provide a declaration of ‘printf’
In file included from sharedServer.h:16,
from mw_wrapperCANChannel.h:16,
from mw_wrapperCANChannel.c:11:
mw_wrapperCANChannel.c: In function ‘canWrite’:
mw_wrapperCANChannel.c:18:23: warning: too many arguments for format [-Wformat-extra-args]
18 | LOG_PRINT(stdout, "REQUEST_CAN_WRITE: n",0);
| ^~~~~~~~~~~~~~~~~~~~~~~
common.h:12:51: note: in definition of macro ‘LOG_PRINT’
12 | #define LOG_PRINT(fd, format, …) fprintf(fd, format, __VA_ARGS__); fflush(fd)
| ^~~~~~
mw_wrapperCANChannel.c:53:26: warning: too many arguments for format [-Wformat-extra-args]
53 | LOG_PRINT(stdout,"CANChannel: Write successful n",0);
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
common.h:12:51: note: in definition of macro ‘LOG_PRINT’
12 | #define LOG_PRINT(fd, format, …) fprintf(fd, format, __VA_ARGS__); fflush(fd)
| ^~~~~~
mw_wrapperCANChannel.c:56:26: warning: too many arguments for format [-Wformat-extra-args]
56 | LOG_PRINT(stdout,"CANChannel: Write failed n",0);
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~
common.h:12:51: note: in definition of macro ‘LOG_PRINT’
12 | #define LOG_PRINT(fd, format, …) fprintf(fd, format, __VA_ARGS__); fflush(fd)
| ^~~~~~
mw_wrapperCANChannel.c: In function ‘canRead’:
mw_wrapperCANChannel.c:65:22: warning: too many arguments for format [-Wformat-extra-args]
65 | LOG_PRINT(stdout,"REQUEST_CAN_Read: n",0);
| ^~~~~~~~~~~~~~~~~~~~~~
common.h:12:51: note: in definition of macro ‘LOG_PRINT’
12 | #define LOG_PRINT(fd, format, …) fprintf(fd, format, __VA_ARGS__); fflush(fd)
| ^~~~~~
mw_wrapperCANChannel.c:98:26: warning: too many arguments for format [-Wformat-extra-args]
98 | LOG_PRINT(stdout,"CANChannel: Read successful n",0);
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
common.h:12:51: note: in definition of macro ‘LOG_PRINT’
12 | #define LOG_PRINT(fd, format, …) fprintf(fd, format, __VA_ARGS__); fflush(fd)
| ^~~~~~
mw_wrapperCANChannel.c:100:26: warning: too many arguments for format [-Wformat-extra-args]
100 | LOG_PRINT(stdout,"CANChannel: Read failed n",0);
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~
common.h:12:51: note: in definition of macro ‘LOG_PRINT’
12 | #define LOG_PRINT(fd, format, …) fprintf(fd, format, __VA_ARGS__); fflush(fd)
| ^~~~~~
picam.c:19:10: fatal error: RaspiCamControl.h: No such file or directory
19 | #include "RaspiCamControl.h"
| ^~~~~~~~~~~~~~~~~~~
compilation terminated.
make: *** [Makefile:81: obj/picam.o] Error 1

STDOUT: make: Entering directory ‘/opt/MATLAB/mw_server_v22.1.0’
[Compiling] IO_wrapperv4l2.c
gcc -c -g -O0 -D_DEBUG -Wall -I/opt/userland -I/opt/userland/host_applications/linux/libs/bcm_host/include
-I/opt/userland/interface/vcos -I/opt/userland/interface/vcos/pthreads -I/opt/userland/interface/vmcs_host/linux
-I/opt/userland/host_applications/linux/apps/raspicam -I/opt/nanomsg/include -Winline -pipe -D_DEBUG -D_MATLABIO_ IO_wrapperv4l2.c
-o obj/IO_wrapperv4l2.o
[Compiling] MW_PWM.c
gcc -c -g -O0 -D_DEBUG -Wall -I/opt/userland -I/opt/userland/host_applications/linux/libs/bcm_host/include
-I/opt/userland/interface/vcos -I/opt/userland/interface/vcos/pthreads -I/opt/userland/interface/vmcs_host/linux
-I/opt/userland/host_applications/linux/apps/raspicam -I/opt/nanomsg/include -Winline -pipe -D_DEBUG -D_MATLABIO_ MW_PWM.c -o
obj/MW_PWM.o
[Compiling] MW_pigs.c
gcc -c -g -O0 -D_DEBUG -Wall -I/opt/userland -I/opt/userland/host_applications/linux/libs/bcm_host/include
-I/opt/userland/interface/vcos -I/opt/userland/interface/vcos/pthreads -I/opt/userland/interface/vmcs_host/linux
-I/opt/userland/host_applications/linux/apps/raspicam -I/opt/nanomsg/include -Winline -pipe -D_DEBUG -D_MATLABIO_ MW_pigs.c -o
obj/MW_pigs.o
[Compiling] sharedServer.c
gcc -c -g -O0 -D_DEBUG -Wall -I/opt/userland -I/opt/userland/host_applications/linux/libs/bcm_host/include
-I/opt/userland/interface/vcos -I/opt/userland/interface/vcos/pthreads -I/opt/userland/interface/vmcs_host/linux
-I/opt/userland/host_applications/linux/apps/raspicam -I/opt/nanomsg/include -Winline -pipe -D_DEBUG -D_MATLABIO_ sharedServer.c
-o obj/sharedServer.o
[Compiling] IO_wrapperCameraboard.c
gcc -c -g -O0 -D_DEBUG -Wall -I/opt/userland -I/opt/userland/host_applications/linux/libs/bcm_host/include
-I/opt/userland/interface/vcos -I/opt/userland/interface/vcos/pthreads -I/opt/userland/interface/vmcs_host/linux
-I/opt/userland/host_applications/linux/apps/raspicam -I/opt/nanomsg/include -Winline -pipe -D_DEBUG -D_MATLABIO_
IO_wrapperCameraboard.c -o obj/IO_wrapperCameraboard.o
[Compiling] mw_wrapperCANChannel.c
gcc -c -g -O0 -D_DEBUG -Wall -I/opt/userland -I/opt/userland/host_applications/linux/libs/bcm_host/include
-I/opt/userland/interface/vcos -I/opt/userland/interface/vcos/pthreads -I/opt/userland/interface/vmcs_host/linux
-I/opt/userland/host_applications/linux/apps/raspicam -I/opt/nanomsg/include -Winline -pipe -D_DEBUG -D_MATLABIO_
mw_wrapperCANChannel.c -o obj/mw_wrapperCANChannel.o
[Compiling] LED.c
gcc -c -g -O0 -D_DEBUG -Wall -I/opt/userland -I/opt/userland/host_applications/linux/libs/bcm_host/include
-I/opt/userland/interface/vcos -I/opt/userland/interface/vcos/pthreads -I/opt/userland/interface/vmcs_host/linux
-I/opt/userland/host_applications/linux/apps/raspicam -I/opt/nanomsg/include -Winline -pipe -D_DEBUG -D_MATLABIO_ LED.c -o
obj/LED.o
[Compiling] picam.c
gcc -c -g -O0 -D_DEBUG -Wall -I/opt/userland -I/opt/userland/host_applications/linux/libs/bcm_host/include
-I/opt/userland/interface/vcos -I/opt/userland/interface/vcos/pthreads -I/opt/userland/interface/vmcs_host/linux
-I/opt/userland/host_applications/linux/apps/raspicam -I/opt/nanomsg/include -Winline -pipe -D_DEBUG -D_MATLABIO_ picam.c -o
obj/picam.o
make: Leaving directory ‘/opt/MATLAB/mw_server_v22.1.0’ raspberry pi, raspi(), matlab MATLAB Answers — New Questions

​

I am trying to compile a Standalone App with App designer.
The goal should be that other user can open the App without having Matlab installed.
The App workes on App Designer perfectly. Even if I compile the App and open it on my computer everything runs.
However, if I open the App on an other computer (without MATLAB, Python and CoolProp) the App opens but the calculation can’t be done.

I guess the problem is the missing Python and/or CoolProp on the second Computer.

My code uses this function:
rho=py.CoolProp.CoolProp.PropsSI(‘D’,’T’,273.15+ T_gc_out ,’P’,p_h,KM)

Is there a way to compile the App including the required Python and CoolProp software to run my programm? I want to include these files, or codes in the app such that the other users don’t have to download Python and CoolProp separately.I am trying to compile a Standalone App with App designer.
The goal should be that other user can open the App without having Matlab installed.
The App workes on App Designer perfectly. Even if I compile the App and open it on my computer everything runs.
However, if I open the App on an other computer (without MATLAB, Python and CoolProp) the App opens but the calculation can’t be done.

I guess the problem is the missing Python and/or CoolProp on the second Computer.

My code uses this function:
rho=py.CoolProp.CoolProp.PropsSI(‘D’,’T’,273.15+ T_gc_out ,’P’,p_h,KM)

Is there a way to compile the App including the required Python and CoolProp software to run my programm? I want to include these files, or codes in the app such that the other users don’t have to download Python and CoolProp separately. I am trying to compile a Standalone App with App designer.
The goal should be that other user can open the App without having Matlab installed.
The App workes on App Designer perfectly. Even if I compile the App and open it on my computer everything runs.
However, if I open the App on an other computer (without MATLAB, Python and CoolProp) the App opens but the calculation can’t be done.

I guess the problem is the missing Python and/or CoolProp on the second Computer.

My code uses this function:
rho=py.CoolProp.CoolProp.PropsSI(‘D’,’T’,273.15+ T_gc_out ,’P’,p_h,KM)

Is there a way to compile the App including the required Python and CoolProp software to run my programm? I want to include these files, or codes in the app such that the other users don’t have to download Python and CoolProp separately. standalone app, app designer, matlab, python function, python, matlab compiler, compiler MATLAB Answers — New Questions

​

In the related Matlab doc, only change of "head" is described.In the related Matlab doc, only change of "head" is described. In the related Matlab doc, only change of "head" is described. visiontransformer, image size, grey images MATLAB Answers — New Questions

​

Hello,
I have data (100X50 array for X, Y and Z coordiantes ) corresponding to the surface shown in the below image. I am interested in obtaining paths from flat region (right side yellow circle) of the surface to the minima at red and brown cricle on the left side of the surface. Only condition is that the path should proceed through lowest value points along the Z axis. I could obtain one path connecting yellow and red circles by using "min" function in matlab. I tried find peaks in order to obtain the other path (shown with black arrow in the image). But, the well around the brown circle is very shallow resulting the findpeaks gives only one peak at the region of red circle. Could you please help me to obtain the second path that connects yellow and brown circles. I have also attached my data file. "Shortestpath" function in matlab might be useful for this purpose. I am however not sure how to use it.
These type of paths are referred as "minimum energy paths" in chemistry, assuming that the Z-axis is energy and X- and Y-axis represent molecular coordiantes. In other words, I am looking for a steepest descent path between yellow and brown circles.

Thank you very much for your time
Regards,
MaheshHello,
I have data (100X50 array for X, Y and Z coordiantes ) corresponding to the surface shown in the below image. I am interested in obtaining paths from flat region (right side yellow circle) of the surface to the minima at red and brown cricle on the left side of the surface. Only condition is that the path should proceed through lowest value points along the Z axis. I could obtain one path connecting yellow and red circles by using "min" function in matlab. I tried find peaks in order to obtain the other path (shown with black arrow in the image). But, the well around the brown circle is very shallow resulting the findpeaks gives only one peak at the region of red circle. Could you please help me to obtain the second path that connects yellow and brown circles. I have also attached my data file. "Shortestpath" function in matlab might be useful for this purpose. I am however not sure how to use it.
These type of paths are referred as "minimum energy paths" in chemistry, assuming that the Z-axis is energy and X- and Y-axis represent molecular coordiantes. In other words, I am looking for a steepest descent path between yellow and brown circles.

Thank you very much for your time
Regards,
Mahesh Hello,
I have data (100X50 array for X, Y and Z coordiantes ) corresponding to the surface shown in the below image. I am interested in obtaining paths from flat region (right side yellow circle) of the surface to the minima at red and brown cricle on the left side of the surface. Only condition is that the path should proceed through lowest value points along the Z axis. I could obtain one path connecting yellow and red circles by using "min" function in matlab. I tried find peaks in order to obtain the other path (shown with black arrow in the image). But, the well around the brown circle is very shallow resulting the findpeaks gives only one peak at the region of red circle. Could you please help me to obtain the second path that connects yellow and brown circles. I have also attached my data file. "Shortestpath" function in matlab might be useful for this purpose. I am however not sure how to use it.
These type of paths are referred as "minimum energy paths" in chemistry, assuming that the Z-axis is energy and X- and Y-axis represent molecular coordiantes. In other words, I am looking for a steepest descent path between yellow and brown circles.

Thank you very much for your time
Regards,
Mahesh minimum value path between two points on 3d grid MATLAB Answers — New Questions

​

Unable to open the requested feature.Check your internet connection and proxy settings in MATLAB Web preferences and then try starting the feature again.
Detailed information:
Error code: -354
Error message: ERR_CONTENT_LENGTH_MISMATCHUnable to open the requested feature.Check your internet connection and proxy settings in MATLAB Web preferences and then try starting the feature again.
Detailed information:
Error code: -354
Error message: ERR_CONTENT_LENGTH_MISMATCH Unable to open the requested feature.Check your internet connection and proxy settings in MATLAB Web preferences and then try starting the feature again.
Detailed information:
Error code: -354
Error message: ERR_CONTENT_LENGTH_MISMATCH installation error MATLAB Answers — New Questions

​