MATLAB: How to simulate default patternnet with feedforwardnet in Matlab

neural networktutorial

I got very different training efficiency with the following network
    net = patternnet(hiddenLayerSize);
and the following one
    net = feedforwardnet(hiddenLayerSize, 'trainscg');
    net.layers{1}.transferFcn = 'tansig';
    net.layers{2}.transferFcn = 'softmax';
    net.performFcn = 'crossentropy';
on the same data.
I was thinking networks should be the same.
What thing I forgot?
UPDATE
The code below shows, that patternnet is systemtically outperforms feedforwardnet. This proves that feedforwardnet is initilized differently somehow. The question is what is the difference?
    hiddenLayerSize = 10;
    % pass 1, with patternnet

    net = patternnet(hiddenLayerSize);
    net.divideParam.trainRatio = 70/100;
    net.divideParam.valRatio = 15/100;
    net.divideParam.testRatio = 15/100;
    [net,tr] = train(net,x,t);
    y = net(x);
    performance = perform(net,t,y);
    fprintf('pass 1, patternnet, performance: %f\n', performance);
    fprintf('num_epochs: %d, stop: %s\n', tr.num_epochs, tr.stop);
    % pass 2, with feedforwardnet

    net = feedforwardnet(hiddenLayerSize, 'trainscg');
    net.layers{1}.transferFcn = 'tansig';
    net.layers{2}.transferFcn = 'softmax';
    net.performFcn = 'crossentropy';
    net.divideParam.trainRatio = 70/100;
    net.divideParam.valRatio = 15/100;
    net.divideParam.testRatio = 15/100;
    [net,tr] = train(net,x,t);
    y = net(x);
    performance = perform(net,t,y);
    fprintf('pass 2, feedforwardnet, performance: %f\n', performance);
    fprintf('num_epochs: %d, stop: %s\n', tr.num_epochs, tr.stop);
    % pass 1, with patternnet
    net = patternnet(hiddenLayerSize);
    net.divideParam.trainRatio = 70/100;
    net.divideParam.valRatio = 15/100;
    net.divideParam.testRatio = 15/100;
    [net,tr] = train(net,x,t);
    y = net(x);
    performance = perform(net,t,y);
    fprintf('pass 3, patternnet, performance: %f\n', performance);
    fprintf('num_epochs: %d, stop: %s\n', tr.num_epochs, tr.stop);
    % pass 2, with feedforwardnet
    net = feedforwardnet(hiddenLayerSize, 'trainscg');
    net.layers{1}.transferFcn = 'tansig';
    net.layers{2}.transferFcn = 'softmax';
    net.performFcn = 'crossentropy';
    net.divideParam.trainRatio = 70/100;
    net.divideParam.valRatio = 15/100;
    net.divideParam.testRatio = 15/100;
    [net,tr] = train(net,x,t);
    y = net(x);
    performance = perform(net,t,y);
    fprintf('pass 4, feedforwardnet, performance: %f\n', performance);
    fprintf('num_epochs: %d, stop: %s\n', tr.num_epochs, tr.stop);
Output follows:
    pass 1, patternnet, performance: 0.116445
    num_epochs: 353, stop: Validation stop.
    pass 2, feedforwardnet, performance: 0.693561
    num_epochs: 260, stop: Validation stop.
    pass 3, patternnet, performance: 0.116445
    num_epochs: 353, stop: Validation stop.
    pass 4, feedforwardnet, performance: 0.693561
    num_epochs: 260, stop: Validation stop.

Best Answer

When comparing numerical designs, you should initialize the RNG to the same initial state before each training.
To compare the properties of FEEDFORWARDNET and PATTERNNET, compare the screen outputs of the following commands WITHOUT THE ENDING SEMICOLON
 net1 = feedforwardnet
 net2 = patternnet
 In particular, compare the following NINE fields
 transferFcn    = net.layers{2}.transferFcn
 yminparam      = net.outputs{2}.processParams{2}.ymin
 yminsetting    = net.outputs{2}.processSettings{2}.ymim
 yrangesetting  = net.outputs{2}.processSettings{2}.yrange
 performFcn     = net.performFcn
 plotFcns       = net.plotFcns
 plotParams     = net.plotParams
 trainFcn       = net.trainFcn
 trainParams    = net.trainParam
Therefore SIX additional fields besides the transferFcn, performFcn and trainFcn must be considered when trying to convert FEEDFORWARDNET to PATTERNNET and vice versa.
Hope this helps.
Thank you for formally accepting my answer
Greg
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