mike_mike asked . 2021-07-13

MLP classification: what is the problem in my code?

1. in the first and second layers a sigmoidal transfer function
2. in the output layer a soft max function"inputs" file is a 3x120 matrix: 3 features and 120 observations" targets" file is a 3x120 matrix (representative of 3 different classes).

1. if I perform view (net) I see that the number of outputs of the output layer is 2 while the output box is 3: what does this mean and why Matlab does this?
2. in the output layer a soft max functionIf I do sum (net ([i1;i2; i3]) I have a value different from 1 but it should be 1 because in the last layer there is a softmax function.

   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 = 'trainbr';  % Scaled conjugate gradient backpropagation.

% Create a Pattern Recognition Network
hiddenLayerSize = [40 20];

net=feedforwardnet(hiddenLayerSize);%crea rete feedforward 
%imposta funzione trasferimento

net.layers{1}.transferFcn = 'tansig'
net.layers{2}.transferFcn = 'tansig'
net.layers{3}.transferFcn = 'softmax'

% 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)
tind = vec2ind(t);
yind = vec2ind(y);
percentErrors = sum(tind ~= yind)/numel(tind);

% View the Network
view(net)

% Plots
% Uncomment these lines to enable various plots.
%figure, plotperform(tr)
%figure, plottrainstate(tr)
%figure, ploterrhist(e)
figure, plotconfusion(t,y)
%figure, plotroc(t,y)

recognition pattern , matlab , deep learning