I have been using neuroph 2.5.1 for a few years with some interesting results.
I apparently can successfully upgrade to 2.6.
I have also tried the latest 2.7 and 2.8. The problem is that from version 2.7 neuroph gives me a flat line as output.
I am using the default set-up for the MultiLayerPerceptron.
In order to migrate from 2.5 to 2.8 I had to replace
TrainingSet with DataSet and SupervisedTrainingElement with DataSetRow.
Everything seemed to go fine until I run the training and calculation which will now constantly give me a flat line output around 0.68/0.69
Do I have to do more adjustments?
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The network is a MultiLayerPerceptron with the following settings :
Training topology : 3,7,1 - MaxIterations :1000 - MaxError :1.0E-4 - LearningRate 0.7
I train using
neuralNetwork.learn(trainingSet)
And calculate using
neuralNetwork.setInput(input);
neuralNetwork.calculate();
Here is a sample of data that would give me some rather consistent results using 2.5.1 as 2.6 but a flat line with versions above.
I have been using neuroph 2.5.1 for a few years with some interesting results.
I apparently can successfully upgrade to 2.6.
I have also tried the latest 2.7 and 2.8. The problem is that from version 2.7 neuroph gives me a flat line as output.
I am using the default set-up for the MultiLayerPerceptron.
In order to migrate from 2.5 to 2.8 I had to replace
TrainingSet with DataSet and SupervisedTrainingElement with DataSetRow.
Everything seemed to go fine until I run the training and calculation which will now constantly give me a flat line output around 0.68/0.69
Do I have to do more adjustments?
There shouldnt be any more adjustments. Can you provide a sample dataset to check? Are you training from code or GUI?
Hi Zoran,
Thank you for your response.
The network is a MultiLayerPerceptron with the following settings :
Training topology : 3,7,1 - MaxIterations :1000 - MaxError :1.0E-4 - LearningRate 0.7
I train using
neuralNetwork.learn(trainingSet)
And calculate using
neuralNetwork.setInput(input);
neuralNetwork.calculate();
Here is a sample of data that would give me some rather consistent results using 2.5.1 as 2.6 but a flat line with versions above.
Training input and desired
Intputs,[1.0, 0.5, 0.0], Output[0.785606821734486]
Intputs,[1.0, 0.5, 0.0], Output[0.5000620713972663]
Intputs,[1.0, 0.0, 0.5], Output[0.5131025160333476]
Intputs,[0.5, 0.0, 1.0], Output[0.4995641418857696]
Intputs,[1.0, 0.0, 0.5], Output[0.6295420187846059]
Intputs,[1.0, 0.5, 0.0], Output[0.4847516561166242]
Intputs,[1.0, 0.5, 0.0], Output[0.5804892254860108]
Intputs,[1.0, 0.5, 0.0], Output[0.4782081020761094]
Intputs,[0.5, 0.0, 1.0], Output[0.5736435728565594]
Intputs,[1.0, 0.0, 0.5], Output[0.4757771426279725]
Intputs,[0.0, 0.5, 1.0], Output[0.53865006410737]
Intputs,[0.0, 1.0, 0.5], Output[0.2938346910218203]
Intputs,[1.0, 0.0, 0.5], Output[0.5728941074890153]
Intputs,[0.5, 0.0, 1.0], Output[0.44013485464426816]
Intputs,[1.0, 0.5, 0.0], Output[0.5372426431741694]
Intputs,[1.0, 0.0, 0.5], Output[0.37939080392011393]
Intputs,[0.0, 1.0, 0.5], Output[0.5282983644135463]
Intputs,[1.0, 0.5, 0.0], Output[0.35835338495081587]
Intputs,[0.0, 0.5, 1.0], Output[0.5653353797656009]
Intputs,[1.0, 0.0, 0.5], Output[0.3032167032332047]
Intputs,[0.0, 1.0, 0.5], Output[0.7561783032837601]
Intputs,[0.0, 1.0, 0.5], Output[0.42256442104474906]
Intputs,[1.0, 0.0, 0.5], Output[0.7196652938196935]
Intputs,[0.5, 1.0, 0.0], Output[0.44018314540365144]
Intputs,[1.0, 0.0, 0.5], Output[0.7040036771373075]
Intputs,[0.0, 0.5, 1.0], Output[0.4025136247274861]
Intputs,[1.0, 0.5, 0.0], Output[0.5227976836751145]
Intputs,[0.5, 1.0, 0.0], Output[0.44067033615604234]
Intputs,[0.5, 1.0, 0.0], Output[0.5412414324703206]
Intputs,[0.5, 1.0, 0.0], Output[0.47676288489048335]
Intputs,[1.0, 0.0, 0.5], Output[0.5419912481910825]
Intputs,[0.0, 1.0, 0.5], Output[0.0]
Intputs,[0.0, 1.0, 0.5], Output[0.6658509258662154]
Intputs,[0.0, 1.0, 0.5], Output[0.10704069893996937]
Intputs,[0.5, 1.0, 0.0], Output[0.5323921738014412]
Intputs,[1.0, 0.5, 0.0], Output[0.49975298003304136]
Intputs,[0.5, 1.0, 0.0], Output[1.0]
Calculation input sample
Caclulation input,[0.0, 0.5, 1.0]
Caclulation input,[0.0, 0.5, 1.0]
Caclulation input,[0.0, 0.5, 1.0]
Caclulation input,[0.0, 0.5, 1.0]
Caclulation input,[0.0, 0.5, 1.0]
Caclulation input,[0.0, 0.5, 1.0]
Caclulation input,[0.0, 0.5, 1.0]
Caclulation input,[0.0, 0.5, 1.0]
Caclulation input,[0.0, 0.5, 1.0]
Caclulation input,[0.0, 0.5, 1.0]
Caclulation input,[0.0, 0.5, 1.0]
Caclulation input,[0.0, 0.5, 1.0]
Caclulation input,[0.0, 0.5, 1.0]
Caclulation input,[0.0, 0.5, 1.0]
Caclulation input,[0.0, 1.0, 0.5]
Caclulation input,[0.0, 1.0, 0.5]
Caclulation input,[0.0, 1.0, 0.5]
Caclulation input,[0.0, 1.0, 0.5]
Caclulation input,[0.0, 1.0, 0.5]
Caclulation input,[0.0, 1.0, 0.5]
Caclulation input,[0.0, 1.0, 0.5]
Caclulation input,[0.0, 1.0, 0.5]
Caclulation input,[0.0, 1.0, 0.5]
Caclulation input,[0.0, 1.0, 0.5]
Caclulation input,[0.0, 1.0, 0.5]
Caclulation input,[0.0, 1.0, 0.5]
Caclulation input,[0.0, 1.0, 0.5]
Caclulation input,[0.0, 1.0, 0.5]
Caclulation input,[0.0, 1.0, 0.5]
Caclulation input,[0.0, 1.0, 0.5]
Caclulation input,[0.0, 1.0, 0.5]
Caclulation input,[0.0, 1.0, 0.5]
Caclulation input,[0.0, 1.0, 0.5]
Caclulation input,[0.0, 1.0, 0.5]
Caclulation input,[0.0, 1.0, 0.5]
Caclulation input,[0.0, 1.0, 0.5]
Caclulation input,[0.0, 1.0, 0.5]
Caclulation input,[0.0, 1.0, 0.5]
Caclulation input,[0.0, 1.0, 0.5]
Caclulation input,[0.0, 1.0, 0.5]
Caclulation input,[0.5, 1.0, 0.0]
Caclulation input,[0.5, 1.0, 0.0]
Caclulation input,[0.5, 1.0, 0.0]
Caclulation input,[0.5, 1.0, 0.0]
Caclulation input,[0.5, 1.0, 0.0]
Caclulation input,[0.5, 1.0, 0.0]
Caclulation input,[0.5, 1.0, 0.0]
Caclulation input,[1.0, 0.5, 0.0]
Caclulation input,[1.0, 0.5, 0.0]
Caclulation input,[1.0, 0.5, 0.0]
Caclulation input,[1.0, 0.5, 0.0]
Caclulation input,[1.0, 0.5, 0.0]
Caclulation input,[1.0, 0.5, 0.0]
Caclulation input,[1.0, 0.5, 0.0]
Caclulation input,[0.5, 1.0, 0.0]
Caclulation input,[0.5, 1.0, 0.0]
Caclulation input,[0.5, 1.0, 0.0]
Caclulation input,[0.5, 1.0, 0.0]
Caclulation input,[0.5, 1.0, 0.0]
Caclulation input,[0.5, 1.0, 0.0]
Caclulation input,[0.0, 1.0, 0.5]
Caclulation input,[0.0, 1.0, 0.5]
Caclulation input,[0.0, 1.0, 0.5]
Caclulation input,[0.0, 1.0, 0.5]
Caclulation input,[0.0, 1.0, 0.5]
Caclulation input,[0.0, 1.0, 0.5]
Caclulation input,[0.0, 1.0, 0.5]
Caclulation input,[0.0, 1.0, 0.5]
Caclulation input,[0.0, 1.0, 0.5]
Caclulation input,[0.0, 1.0, 0.5]
Caclulation input,[0.0, 1.0, 0.5]
Caclulation input,[0.0, 1.0, 0.5]
Caclulation input,[0.0, 1.0, 0.5]
Caclulation input,[0.0, 1.0, 0.5]
Caclulation input,[0.0, 1.0, 0.5]
Caclulation input,[0.0, 1.0, 0.5]
Caclulation input,[0.0, 1.0, 0.5]
Caclulation input,[0.0, 1.0, 0.5]
Caclulation input,[0.0, 1.0, 0.5]
Caclulation input,[0.0, 1.0, 0.5]
Caclulation input,[0.0, 1.0, 0.5]
Caclulation input,[0.0, 1.0, 0.5]
Caclulation input,[0.0, 1.0, 0.5]
Caclulation input,[0.0, 1.0, 0.5]
Caclulation input,[0.0, 1.0, 0.5]
Caclulation input,[0.0, 1.0, 0.5]
Caclulation input,[0.0, 1.0, 0.5]
I am doing something wrong here?
Last edit: guillaume T 2014-05-15
Hi Zoran,
Thanks for the good work.
Did you have a chance to look into this?