MATLAB: Optimal hidden nodes number

basic assumptionsDeep Learning Toolboxhidden nodes

Hello everybody,
In order to determine optimal hidden neurons, Trial and error algorithm has been used (trial = 10, 10 < H < 100, dH = 100). I get the table on top but i can not determine the optimal hidden neurons. The table contains (Trials, Hidden neurons, test_mse, train_mse, val_mse, test_R, train_R, val_R)
Please i need your help. Thank you.

Best Answer

I have posted hundreds of examples in both the NEWSGROUP (comp.soft-sys.matlab) and ANSWERS that determine the optimal number of hidden nodes defined by
 1. One Hidden Layer (ALWAYS SUFFICIENT!!!)
 2. Minimum Number of Hidden Nodes subject to my  
    practicality constraint
    TRAINING SUBSET RSQUARE >= 0.99 
    i.e.
    99% of the training subset target variance is
    successfully modeled by the net.
    Equivalently
    TRAINING SUBSET MSE <= 0.01*TRAINING SUBSET VARIANCE
 3. COMMENTS & CAVEATS
    a. The training subset must be a good representative of 
validation and test data
    b. A smaller number of hidden nodes can often be obtained 
by using multiple hidden layers
    c. The MSE minimization technique used for regression and 
curvefitting (e.g., via FITNET)is also successful for classification 
and pattern recognition (e.g., via PATTERNNET) where the 
minimization function is cross-entropy and the desired result is 
minimal error rate.
4. Suggested NEWSGROUP and ANSWERS search words for either FITNET or PATTERNNET
greg fitnet/patternnet msegoal nmse
5. The method is also used for timeseries
Hope this helps.
Thank you for formally accepting this answer
Greg
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