How to set output size in Matlab newff method - matlab

Question

Summary:
I'm trying to do classification of some images depending on the angles between body parts.
I assume that human body consists of 10 parts(as rectangles) and find the center of each part and calculate the angle of each part by reference to torso.
And I have three action categories:Handwave-Walking-Running.
My goal is to find which test images fall into which action category.
Facts:
TrainSet:1057x10 feature set,1057 stands for number of image.
TestSet:821x10
I want my output to be 3x1 matrice each row showing the percentage of classification for action category.
row1:Handwave
row2:Walking
row3:Running
Code:
actionCat='H';
[train_data_hw train_label_hw] = tugrul_traindata(TrainData,actionCat);
[test_data_hw test_label_hw] = tugrul_testdata(TestData,actionCat);
actionCat='W';
[train_data_w train_label_w] = tugrul_traindata(TrainData,actionCat);
[test_data_w test_label_w] = tugrul_testdata(TestData,actionCat);
actionCat='R';
[train_data_r train_label_r] = tugrul_traindata(TrainData,actionCat);
[test_data_r test_label_r] = tugrul_testdata(TestData,actionCat);
Train=[train_data_hw;train_data_w;train_data_r];
Test=[test_data_hw;test_data_w;test_data_r];
Target=eye(3,1);
net=newff(minmax(Train),[10 3],{'logsig' 'logsig'},'trainscg');
net.trainParam.perf='sse';
net.trainParam.epochs=50;
net.trainParam.goal=1e-5;
net=train(net,Train);
trainSize=size(Train,1);
testSize=size(Test,1);
if(trainSize > testSize)
pend=-1*ones(trainSize-testSize,size(Test,2));
Test=[Test;pend];
end
x=sim(net,Test);
Question:
I'm using Matlab newff method.But my output is always an Nx10 matrice not 3x1.
My input set should be grouped as 3 classes but they are grouped to 10 classes.
Thanks

Answer 1

%% Load data : I generated some random data instead
Train = rand(1057,10);
Test = rand(821,10);
TrainLabels = randi([1 3], [1057 1]);
TestLabels = randi([1 3], [821 1]);
trainSize = size(Train,1);
testSize = size(Test,1);
%% prepare the input/output vectors (1-of-N output encoding)
input = Train'; %'matrix of size numFeatures-by-numImages
output = zeros(3,trainSize); % matrix of size numCategories-by-numImages
for i=1:trainSize
output(TrainLabels(i), i) = 1;
end
%% create net: one hidden layer with 10 nodes (output layer size is infered: 3)
net = newff(input, output, 10, {'logsig' 'logsig'}, 'trainscg');
net.trainParam.perf = 'sse';
net.trainParam.epochs = 50;
net.trainParam.goal = 1e-5;
view(net)
%% training
net = init(net); % initialize
[net,tr] = train(net, input, output); % train
%% performance (on Training data)
y = sim(net, input); % predict
%[err cm ind per] = confusion(output, y);
[maxVals predicted] = max(y); % predicted
cm = confusionmat(predicted, TrainLabels);
acc = sum(diag(cm))/sum(cm(:));
fprintf('Accuracy = %.2f%%\n', 100*acc);
fprintf('Confusion Matrix:\n');
disp(cm)
%% Testing (on Test data)
y = sim(net, Test');
Note how I converted from category label for each instance (1/2/3) to a 1-to-N encoding vector ([100]: 1, [010]: 2, [001]: 3)
Also note that the test set is currently not being used, since by default the input data is divided into train/test/validation. You could achieve your manual division by setting net.divideFcn to the divideind function, and setting the corresponding net.divideParam parameters.
I showed the testing on the same training data, but you could do the same for the Test data.