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I am trying to implement a Neural Network. I am currently working on the backpropagation part. I don't need help with the coding. I have wrote the feedForward part so far with great success. But my question more related to the dataset I am using.

this is my data set:

http://archive.ics.uci.edu/ml/machine-learning-databases/cpu-performance/machine.data

I have to use a 5-fold cross validation to backpropagate and stop training until error threshold is .1, .01, .001 meaning I have to do 3 trials. Ignore the first 2 point for each data point. I have already normalized the set. The architecture of the network is 7 neuron in input layer, 3 neurons in 1 hidden layer and 1 output. Very basic implementation.

So my question is,

  1. I have to break the set up in 5 smaller subset right? keep one for testing and rest for validation and training right?

  2. how long do I train? say I use 1 fold (about 42 data sets per fold) and i reach my desired error threshold. Do I stop? and use the test data? else I load up the next set and keep training? what if I run out of data set before I reach my desired error threshold?

  3. also should I follow something like this?

    1. use input values
    2. feed forward, then compare error
    3. backpropagate and adjust weights
    4. repeat process a-c until i reach error threshold and finish all data in the current fold?

thank you for your time and response. I am really trying to understand how to use the dataset. I will update you guys once I have written the code.

S.Kahn
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  • I tried to make sense of your question by formatting but it looks like you're asking for a lot of things. Try to focus on one thing and put your question clearly. In any case - I came upon this question because of the java tag but I don't see anything related to Java in it, so I will remove the tag. – Erwin Bolwidt Nov 05 '16 at 04:14
  • Thank you so much for formatting my question Erwin Bolwidt. – S.Kahn Nov 05 '16 at 04:16

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