Why does the TRAIN function within the Neural Networks Toolbox return incorrect converged errors and network outputs when using BFGS training?

2009 6 27
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更新时间：2014 1 15
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My neural network uses the TRAINBFG function as its "trainFcn" property. After training, the output argument "E" should contain the final network output errors, and "Y" should contain the converged network outputs. They actually contain the initial output errors and outputs.
The following code illustrates the problem:
p = [0 1 2 3 4 5 6 7 8];
t = [0 0.84 0.91 0.14 -0.77 ...
-0.96 -0.28 0.66 0.99];
net = newff([0 8],[10 1],...
{'tansig' 'purelin'},'trainbfg');
net.trainParam.epochs = 50;
net.trainParam.goal = 0.01;
[net,tr,Y,E,Pf,Af] = train(net,p,t);
y2=sim(net,p);
final_outputs = y2
Y
final_error = t-y2
E
This results in:
final_outputs =
Columns 1 through 3
0.0830 0.7615 0.8620
Columns 4 through 6
0.0412 -0.6370 -0.8537
Columns 7 through 9
-0.1970 0.6032 1.0597
Y =
Columns 1 through 3
1.8211 0.7829 -0.7174
Columns 4 through 6
-2.5872 -3.1501 -4.4639
Columns 7 through 9
-5.0563 -4.0460 -4.6061
final_error =
Columns 1 through 3
-0.0830 0.0785 0.0480
Columns 4 through 6
0.0988 -0.1330 -0.1063
Columns 7 through 9
-0.0830 0.0568 -0.0697
E =
Columns 1 through 3
-1.8211 0.0571 1.6274
Columns 4 through 6
2.7272 2.3801 3.5039
Columns 7 through 9
4.7763 4.7060 5.5961
The converged error is "final_error", whose elements are the differences between the converged network outputs and the targets. The values in the "E" output argument of TRAINBFG should be equal to those in "final_error".

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 采纳的回答

0 个投票

This is a bug in the way the TRAINBFG function within the Neural Network Toolbox updates the network outputs and errors during training.
Currently, to work around this, use the SIM function to find the network outputs after the network has completed training. The final errors are computed by finding the difference between the final outputs and the targets. The following sample code illustrates this:
p = [0 1 2 3 4 5 6 7 8];
t = [0 0.84 0.91 0.14 ...
-0.77 -0.96 -0.28 ...
0.66 0.99];
net = newff([0 8],[10 1],...
{'tansig' 'purelin'},'trainbfg');
net.trainParam.epochs = 50;
net.trainParam.goal = 0.01;
[net,tr,Y,E,Pf,Af] = train(net,p,t);
y2=sim(net,p);
final_outputs = y2
final_error = t-y2

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