I am Designging a multi-ouput ANN for regression and classification simultaneously but i face error

Question

Ameer HAmza 2024-3-12

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/2093651-i-am-designging-a-multi-ouput-ann-for-regression-and-classification-simultaneously-but-i-face-error

回答： Ameer HAmza 2024-8-21

clc;clear all;
%% Read file
filename = "CorrectDS.csv";
tbl = readtable(filename,'TextType','String');
head(tbl)
X=table2array(tbl);
training=X(:,1:23);
classLabel=categorical(X(:,24));
regLabel=X(:,25);
num_features = size(X, 2)-2;
num_classes = 2;
num_neurons_hidden = 20;
% Define the neural network architecture
layers = [
    featureInputLayer(num_features, 'Normalization', 'none')
    fullyConnectedLayer(num_neurons_hidden)
    reluLayer
    fullyConnectedLayer(num_neurons_hidden)
    reluLayer
    fullyConnectedLayer(num_neurons_hidden)  % Additional hidden layer for classification
    reluLayer
    fullyConnectedLayer(num_classes)        % Classification output
    softmaxLayer
    fullyConnectedLayer(1)                  % Regression output
    regressionLayer
    ];
options = trainingOptions('adam', ...
    'MaxEpochs', 50, ...
    'MiniBatchSize', 16, ...
    'Verbose', true, ...
    'Plots', 'training-progress');
lab={classLabel,regLabel};
net = trainNetwork(training,lab,layers, options);

and I got this error

Error using trainNetwork
Invalid training data. For classification tasks, responses must be a vector of categorical responses. For regression tasks, responses must be a vector, a matrix,
or a 4-D array of numeric responses which must not contain NaNs.
Error in MAin (line 40)
net = trainNetwork(training,lab,layers, options);

Please, Someone help me to tackle this error

8 个评论
显示 6更早的评论隐藏 6更早的评论

Walter Roberson 2024-3-13

You probably have to split the regression and the classification.

Ameer HAmza 2024-3-13

ok thank you

请先登录，再进行评论。

请先登录，再回答此问题。

Answer 1

Kaustab Pal 2024-8-21

0
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/2093651-i-am-designging-a-multi-ouput-ann-for-regression-and-classification-simultaneously-but-i-face-error#answer_1502384

在 MATLAB Online 中打开

Hi @Ameer HAmza,

To create a Neural Network capable of both classification and regression, you should design the network with two distinct branches: one dedicated to classification and the other to regression. You can utiize the "dlnetwork" function to achieve this. Below is a sample code to illustrate how you can implement this:

num_classes = 3;
num_neurons_hidden = 20;
% Define the neural network architecture
net = dlnetwork();
tmp_layers = [
    featureInputLayer(10, 'Normalization', 'none', "Name","input_layer")
    fullyConnectedLayer(num_neurons_hidden,"Name","fc1")
    reluLayer("Name","relu_1")
    fullyConnectedLayer(num_neurons_hidden, "Name","fc2")
    reluLayer("Name","relu_2")
    fullyConnectedLayer(num_neurons_hidden, "Name","fc3")  % Additional hidden layer for classification
    reluLayer("Name","relu_3")
    fullyConnectedLayer(num_classes, "Name","fc4")        % Classification output
    softmaxLayer("Name","classifier_output")
    ];
net = addLayers(net,tmp_layers);
tempLayers = [
    fullyConnectedLayer(1, "Name","regression_output") 
    ];
net = addLayers(net,tempLayers);
net = connectLayers(net,"fc4","regression_output");
analyzeNetwork(net)