MATLAB: If i trained the neural network and i get the weights matrix and biases matrix, how i can use these matrices in predicting new data in new network if i do not have the training data later ?

how i can use these matrices in predicting new data in new network if i do not have the training data later ??if i trained the neural network and i get the weights matrix and biases matrix

if i trained the neural network and i get the weights matrix and biases matrix, how i can use these matrices in predicting new data in new network if i do not have the training data later ??

Best Answer

In the following, notice the difference between design data and training data
NOMENCLATURE:
   data      = design + nondesign 
   design    = training + validation
   nondesign = testing + unseen
   data        = training + nontraining
   nontraining = validation + testing + unseen
ASSUMPTIONS: The most important assumptions in NEURAL NETWORK DESIGN are
 1. All relevant data ( i.e., training, validation, testing and unseen ) are 
    assumed to be random samples from the same probability distribution.
 2. Training data are assumed to be a representative sample of the whole 
    distribution.( However, performance estimates CAN BE EXTREMELY BIASED 
    because the same data is used for training and estimation).
 3. Validation data are nontraining design data that are a sufficient 
    representative of nondesign data so that training is sufficient for the
    model to be able to generalize to all relevant data. (Performance 
   estimates tend to be SLIGHTLY BIASED because validation data are still a 
   design data subset).
4. Testing data are nondesign data that are used to obtain UNBIASED 
   performance estimates on all nontraining data (validation + testing 
  + unseen);
5. Now, to answer your question: If the above assumptions hold, the trained 
 net is completely represented by weights corresponding to the assumed 
 architecture. 
    Therefore if you suspect new data may not obey the assumptions, you need 
   a prequalifier stage to verify the input is likely to have come from the 
   appropriate probability distribution. This can be achieved in a variety 
   of ways by checking summary statistical characteristics like min, median, 
   mean, standard deviation and max or Mahalanobis distance to the original 
   distribution.
Hope this helps.
Thank you for formally accepting my answer
Greg
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