How to reach train and test and their predictions in nftool?

Question

Malik 2024-7-20

0
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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/2138986-how-to-reach-train-and-test-and-their-predictions-in-nftool

编辑： Muskan 2024-7-22

I have conducted neural network on my dataset with 228 rows and 7 columns but don't know how to obtain my training and testing datasets and their prediction values. I want to export this values.

% Solve an Input-Output Fitting problem with a Neural Network
% Script generated by Neural Fitting app
% Created 20-Jul-2024 16:31:01
%
% This script assumes these variables are defined:
%
%   data - input data.
%   data_1 - target data.
x = data';
t = data_1';
% Choose a Training Function
% For a list of all training functions type: help nntrain
% 'trainlm' is usually fastest.
% 'trainbr' takes longer but may be better for challenging problems.
% 'trainscg' uses less memory. Suitable in low memory situations.
trainFcn = 'trainlm';  % Levenberg-Marquardt backpropagation.
% Create a Fitting Network
hiddenLayerSize = 4;
net = fitnet(hiddenLayerSize,trainFcn);
% Setup Division of Data for Training, Validation, Testing
net.divideParam.trainRatio = 70/100;
net.divideParam.valRatio = 15/100;
net.divideParam.testRatio = 15/100;
% Train the Network
[net,tr] = train(net,x,t);
% Test the Network
y = net(x);
e = gsubtract(t,y);
performance = perform(net,t,y)
% View the Network
view(net)
% Plots
% Uncomment these lines to enable various plots.
%figure, plotperform(tr)
%figure, plottrainstate(tr)
%figure, ploterrhist(e)
%figure, plotregression(t,y)
%figure, plotfit(net,x,t)

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Answer 1

Muskan 2024-7-22

1
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此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/2138986-how-to-reach-train-and-test-and-their-predictions-in-nftool#answer_1488656

编辑：Muskan 2024-7-22

在 MATLAB Online 中打开

Hi,

As per my understanding you can follow the following steps to obtain the training and testing datasets along with their prediction values:

Extract Training and Testing Indices: Use the "tr" structure returned by the "train" function to get the indices of the training, validation, and testing datasets.
Export Data and Predictions: Use these indices to extract the corresponding data and predictions, then save them to a file.

Here's how you can modify your script to achieve this:

% Extract Training, Validation, and Testing Indices
trainInd = tr.trainInd;
valInd = tr.valInd;
testInd = tr.testInd;
% Extract Training, Validation, and Testing Data
x_train = x(:, trainInd);
t_train = t(:, trainInd);
y_train = y(:, trainInd);
x_val = x(:, valInd);
t_val = t(:, valInd);
y_val = y(:, valInd);
x_test = x(:, testInd);
t_test = t(:, testInd);
y_test = y(:, testInd);
% Export Data and Predictions
train_data = [x_train' t_train' y_train'];
val_data = [x_val' t_val' y_val'];
test_data = [x_test' t_test' y_test'];

After this, if you want to save the results in any file, you can use the "writematrix" function. Kindly refer to the documentation of "writematrix" for more information: https://www.mathworks.com/help/matlab/ref/writematrix.html