Q:

Is it normal to get big error in backpropagation neural network when I using the same data training and data test?

I'm doing some programming with neural network backpropagation.
I have about 90 datas and doing some training with all data for data training (90 datas) and same data for data test (90 datas). I'm using iteration threshold about 2 iteration to test it and it gave me quite big error (About 60% with MAPE/Mean Absolute Square Error).
I'm afraid I've got the algorithm wrong since the only way to get training error less than threshold 10% is using iteration threshold around 3000k iteration and it's training takes quite a long time (I'm not using momentum. Just a Backpropagation Neural Network). But the test accuracy around 95-99% after that using said condition.
Is this normal? Or my program is work as it shouldn't be?

A:

Of course, it will depend on the data set used, but I wouldn't be surprised if you get an error below 1% even for highly nonlinear data (I've seen this for example in sales data). As long as you separate training and test data sets, the error is expected to rise, but with the same set, it should drop to zero if there are enough hidden units. The capacity of an ANN to fit nonlinear data is huge (and, of course, the more fitted, the less general).
So, I would look for some program bug instead.

