Trying to get prediction scatter plot

clc; clear all; close all;
%Import/Upload data
load generated_data.mat
% change to label vector
CS = categories(categorical(Y1));
Z1 = []; Z2 = [];
for i = 1 : length(Y1)
    Z1(i,1) = find(Y1(i)==CS);
end
for i = 1 : length(Y2)
    Z2(i,1) = find(Y2(i)==CS);
end
Yo1 = Y1;
Yo2 = Y2;
Y1 = Z1;
Y2 = Z2;
%transposing glucose data
X1_T = X1';
%Shuffling data to take randomly
rand('seed', 0)
ind = randperm(size(X1_T, 1));
X1_T = X1_T(ind, :);
Y1 = Y1(ind);
%Separating data in training, validation and testing data
X1_train = X1_T;
%Partioning data for training 70%
train_X1 = X1_train(1:120,:);
%Corresponding X(input) data to Y(output) data
train_Y1 = Y1(1:120);
%reshaping data into 4D array
XTrain=(reshape(train_X1', [2289,1,1,120])); 
%Separating and partioning for validation data 15%
val_X1 = X1_train(121:150,:);
%Corresponding X(input) data to Y(output) data
val_Y1 = Y1(121:150);
%reshaping data into 4D array
XVal=(reshape(val_X1', [2289,1,1,30])); %Train data
%Separating and partioning for test data 15%
test_X1 = X1_train(151:180,:);
%Corresponding X(input) data to Y(output) data
test_Y1 = Y1(151:180);
%reshaping data into 4D array
XTest=(reshape(test_X1', [2289,1,1,30])); %Train data
%% NETWORK ARCHITECTURE
layers = [imageInputLayer([2289 1 1])  % Creating the image layer
 convolution2dLayer([102 1],3,'Stride',1)
    batchNormalizationLayer
    reluLayer
    maxPooling2dLayer(2,'Stride',2,'Padding',[0 0 0 1])
    dropoutLayer
    fullyConnectedLayer(1)
    regressionLayer];
% Specify training options.
opts = trainingOptions('sgdm', ...
    'MaxEpochs',1500, ...
    'Shuffle','every-epoch', ...
    'Plots','training-progress', ...
    'Verbose',false, ...
    'ValidationData',{XVal,val_Y1},...
    'ExecutionEnvironment', 'cpu', ...
    'ValidationPatience',Inf);
%% Train network
%net = trainNetwork(XTrain,Trainoutfinal,layers,opts);
yc = train_Y1(:);
net1 = trainNetwork(XTrain,yc,layers,opts);
%% Compare against testing Data
miniBatchSize =27;
YPred = predict(net1,XTest, ...
    'MiniBatchSize',miniBatchSize,...
    'ExecutionEnvironment', 'cpu');

Ypredicted = predict(net, XTrain)
predictionError = double(testoutfinal) - test_Y1(:);
squares = predictionError.^2;
rmse = sqrt(mean(squares))

figure
scatter(Ypredicted, double(testoutfinal),'+')
title ('True value vs Predicted Value')
xlabel ("Predicted Value")
ylabel ("True Value")

hold on
plot([-3 3], [-3 3], 'b--')

[Nathaniel Porter]

I noticed that the question was edited?

[KSSV]

Ypredicted = predict(net, XTrain)

If you have the target/ output for XTrain. Then you can plot right?

Answer 1

[yanqi liu]

clc; clear all; close all;
%Import/Upload data
load generated_data.mat
% change to label vector
CS = categories(categorical(Y1));
Z1 = []; Z2 = [];
for i = 1 : length(Y1)
    Z1(i,1) = find(Y1(i)==CS);
end
for i = 1 : length(Y2)
    Z2(i,1) = find(Y2(i)==CS);
end
Yo1 = Y1;
Yo2 = Y2;
Y1 = Z1;
Y2 = Z2;
%transposing glucose data
X1_T = X1';
%transposing insulin data
X2_T = X2';
rand('seed', 0)
ind = randperm(size(X1_T, 1));
X1_T = X1_T(ind, :);
Y1 = Y1(ind);
%Separating data in training, validation and testing data
X1_train = X1_T;
%Partioning data for training
train_X1 = X1_train(1:120,:);
train_Y1 = Y1(1:120);
%DataParts = zeros(size(Train_inputX1,1), size(Train_inputX1,2),1,2); %(4500,400,1,2)
%DataParts(:,:,:,1) = real(cell2mat(Train_inputX1));
%DataParts(:,:,:,2) = imag(cell2mat(Train_inputX1)) ;
XTrain=(reshape(train_X1', [2289,1,1,120])); %Train data
%Separating and partioning for validation data
val_X1 = X1_train(121:150,:);
val_Y1 = Y1(121:150);
XVal=(reshape(val_X1', [2289,1,1,30])); %Train data
%Separating and partioning for test data
test_X1 = X1_train(151:180,:);
test_Y1 = Y1(151:180);
XTest=(reshape(test_X1', [2289,1,1,30])); %Train data
%% NETWORK ARCHITECTURE
layers = [imageInputLayer([2289 1 1])  % Creating the image layer
 convolution2dLayer([102 1],3,'Stride',1)
    batchNormalizationLayer
    reluLayer
    maxPooling2dLayer(2,'Stride',2,'Padding',[0 0 0 1])
    dropoutLayer
    fullyConnectedLayer(1)
    regressionLayer];
% Specify training options.
opts = trainingOptions('adam', ...
    'MaxEpochs',1500, ...
    'Shuffle','every-epoch', ...
    'Plots','training-progress', ...
    'Verbose',false, ...
    'ValidationData',{XVal,val_Y1},...
    'ExecutionEnvironment', 'cpu', ...
    'ValidationPatience',Inf);
%% Train network
%net = trainNetwork(XTrain,Trainoutfinal,layers,opts);
yc = train_Y1(:);
net1 = trainNetwork(XTrain,yc,layers,opts);
%% Compare against testing Data
miniBatchSize =27;
testoutfinal = predict(net1,XTest, ...
    'MiniBatchSize',miniBatchSize,...
    'ExecutionEnvironment', 'cpu');
predictionError = testoutfinal(:) - test_Y1(:);
squares = predictionError.^2;
rmse = sqrt(mean(squares))

figure
scatter(test_Y1, round(testoutfinal),'+')
title ('True value vs Predicted Value')
xlabel ("True Value")
ylabel ("Predicted Value")

hold on; box on;
plot([1 6], [1 6], 'b--')