THE DIFFERACE RESULT IN NEURAL NETWORK PROBLEM

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primrose khaleed
primrose khaleed el 19 de Jun. de 2014
Editada: Cedric el 21 de Jun. de 2014
Hi
I hope someone can help me with my question.
When I run the backprop neural network more than once on the same data set i get a different set of results. (the predicted results are different each time). Is there a way to train the Neural Network to output the same (lowest error predictions) if you run the code more than once for the same data set ? when enter the testimage for first time will classify as first class but when rerun the program with same testimage will classify as second class.. how can solve this problem ...this is my file Thanks

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Greg Heath
Greg Heath el 19 de Jun. de 2014
if you are using a saved net that has been previously trained, this should not happen.
If you are setting the RNG to the same initial state before retraining, this should not happen.
Hope this helps.
Thank you for formally accepting my answer
Greg
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Greg Heath
Greg Heath el 21 de Jun. de 2014
What are the sizes of your training, validation and test sets?
What range of hidden node values are you searching over?
How many random initial weight initializations for each hidden node value?
What are the trn/val/test R-squared values for the "best" (i.e. max(R2val)) design?
primrose khaleed
primrose khaleed el 21 de Jun. de 2014
Editada: Cedric el 21 de Jun. de 2014
thank you greg.. the size of trainig ,validation and test is:
mynet.divideParam.trainRatio = 70/100;
mynet.divideParam.valRatio = 15/100;
mynet.divideParam.testRatio = 15/100;
the hidden node i used the defult (=10)
but i have the stuped quetions : i dont underetand How many random initial weight initializations for each hidden node value?
What are the trn/val/test R-squared values for the "best" (i.e. max(R2val)) design?
how can random initial wights??? the matlab do not do it???
plz help me greg

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