How to test neural network trained model?

Hi

I am using NN for classification purpose, i know how to do this for one subject, by didviding the data in training:testing:validation sets. But i want to train my network on one subject's entire data and test it on the other subject's data.

I am confused how to do this.? How to pass on the labels and test input to the model.?

I have searched and i learned to save the network, then load it and run with the new test set as an input. I get that but what about the labels, how should i pass on the new labels for the test set.?

Please help.!

Following is the code:

clear all

trainFcn = 'trainscg'; % Scaled conjugate gradient backpropagation.

hiddenLayerSize = [10 10 10];

net = patternnet(hiddenLayerSize, trainFcn);

% Choose Input and Output Pre/Post-Processing Functions

% For a list of all processing functions type: help nnprocess

net.input.processFcns = {'removeconstantrows','mapminmax'};

%% Setup Division of Data for Training, Validation, Testing

% For a list of all data division functions type: help nndivision

net.divideFcn = 'dividerand'; % Divide data randomly

net.divideMode = 'sample'; % Divide up every sample

net.divideParam.trainRatio = 70/100;

net.divideParam.valRatio = 10/100;

net.divideParam.testRatio = 20/100;

%% Choose a Performance Function

% For a list of all performance functions type: help nnperformance

net.performFcn = 'crossentropy'; % Cross-Entropy

net.trainParam.max_fail = 300;

net.trainParam.min_grad = 0.00000001;

% Choose Plot Functions

% For a list of all plot functions type: help nnplot

net.plotFcns = {'plotperform','plottrainstate','ploterrhist', ...

'plotconfusion', 'plotroc'};

%% Train the Network

[net,tr] = train(net,Input_Signals,Labels);

%% Test the Network

y = net(Input_Signals);

e = gsubtract(Labels,y);

performance = perform(net,Labels,y)

tind = vec2ind(Labels);

yind = vec2ind(y);

percentErrors = sum(tind ~= yind)/numel(tind);

%% Recalculate Training, Validation and Test Performance

trainTargets = Labels .* tr.trainMask{1};

valTargets = Labels .* tr.valMask{1};

testTargets = Labels .* tr.testMask{1};

trainPerformance = perform(net,trainTargets,y)

valPerformance = perform(net,valTargets,y)

testPerformance = perform(net,testTargets,y)

%% View the Network

view(net)

Answer 1

[Srivardhan Gadila]

In "Test the Network section" from the above code of yours, replace Input_Signals with new test set and Labels with the labels of new test set.

Refer to Classify Patterns with a Shallow Neural Network Using Command-Line Functions.

[Joana]

Hi

I did that, but it classifies everything as one class, resulting the 50% accuracy. Although i am training and testing the model with equal number of samples for each class.

Any idea what could be the problem.?

I implemented LDA as well, with training on one set and testing on other, but results are same as 50-53%. i don't know why.?