perform neural network verification in matlab

To perform neural network verification in MATLAB, you can follow these steps:

1. Define your neural network model using the MATLAB neural network toolbox or by building your own custom model.
2. Split your data into training, validation, and testing sets using the dividerand function or the crossvalind function.
3. Train your neural network model using the training dataset and the train function.
4. Validate your neural network model using the validation dataset and the validate function. This step helps you tune the hyperparameters of your model to avoid overfitting.
5. Test your neural network model using the testing dataset and the sim function. This step evaluates the performance of your model on unseen data.

Here is some sample code that demonstrates these steps:

main.m
% Load example data
load iris_dataset

% Split data into training, validation, and testing sets
[trainInd,valInd,testInd] = dividerand(size(irisInputs,2),0.6,0.2,0.2);
trainInputs = irisInputs(:,trainInd);
trainTargets = irisTargets(:,trainInd);
valInputs = irisInputs(:,valInd);
valTargets = irisTargets(:,valInd);
testInputs = irisInputs(:,testInd);
testTargets = irisTargets(:,testInd);

% Define neural network model
net = patternnet(10);

% Train neural network model
net = train(net,trainInputs,trainTargets);

% Validate neural network model
valOutputs = net(valInputs);
valPerformance = perform(net,valTargets,valOutputs);

% Test neural network model
testOutputs = net(testInputs);
testPerformance = perform(net,testTargets,testOutputs);
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In this example, we load the iris dataset and split it into training, validation, and testing sets. We then define a neural network model with 10 hidden neurons using the patternnet function. We train the model using the training dataset and validate it using the validation dataset. Finally, we evaluate the performance of the model on the testing dataset.