Prashant Kumar answered . 2024-05-18 12:05:17

load cancer_dataset;
% 2 neurons in the first layer (tansig) and 1 neuron in the second layer
% (purelin).
% Levenberg-Maquardt Backpropagation Method is used
mlp_net = newff(cancerInputs,cancerTargets,2,{'tansig'},'trainlm');

% Different sets are randomly created for training, validation and testing
% the network
mlp_net.divideParam.trainRatio = 0.6;
mlp_net.divideParam.valRatio = 0.2;
mlp_net.divideParam.testRatio = 0.2;
mlp_net.trainparam.epochs = 100;
[mlp_net,tr] = train(mlp_net,cancerInputs,cancerTargets);

% Once the network has been trained, we can obtain the Mean Squared Error
% for the best epoch (time when the training has stopped in order to avoid
% overfitting the network).
mse_train = tr.perf(tr.best_epoch + 1); % There is epoch 0, but arrays in 
                                        % MATLAB start in 1.
mse_val = tr.vperf(tr.best_epoch + 1);
mse_test = tr.tperf(tr.best_epoch + 1);

>> mlp_net.divideParam

ans = 

      Function Parameters for 'dividerand'

      Training Ratio trainRatio: 0.6
      Validation Ratio valRatio: 0.2
      Test Ratio      testRatio: 0.2

By the way, if you are using MATLAB R2011a, you should use feedforwardnet instead of newff.

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