The ultimate goal is too obtain a net that performs well on non-training data that comes from the same or similar source as the training data. This is called GENERALIZATION.
Frequent causes of failure are
1. Not enough weights to adequately characterize the training data
2. Training data does not adequately characterize the salient features of non-training data because of measurement error, transcription error, noise, interference, insufficient sampling size and variability
3. Fewer training equations than unknown weights.
4. Random weight initialization
Various techniques use to mitigate these causes are
1. Remove bad data and outliers (plots help)
2. Use enough training data to sufficiently characterize non-training data.
3. Use enough weights to adequately characterize the training data
4. Use more training equations than unknown weights. The stability of
solutions w.r.t. noise and errors increases as the ratio increases.
5. Use the best of multiple random initialization & data-division designs
6. K-fold Cross-validation
7. Validation Stopping
8. Regularization
For the iris_dataset
[ I N ] = size(input) % [ 4 150 ]
[ O N ] = size(target) % [ 3 150 ]
Assuming the default 0.7/0.15/0.15 trn/val/tst data division, the number of training equations is approximately
Ntrneq = 0.7*N*O % 315
Assuming the default I-H-O node topology, the number of unknown equations is
Nw = (I+1)*H+(H+1)*O = (I+O+1)*H + O
Obviously, Nw < Ntrneq when H <= Hub (upper bound) where
Hub = floor( (Ntrneq-O)/(I+O+1)) % 39
Expecting decent solutions for H <= 20 seems reasonable. However, to
mitigate the random initial weights and data division, design 10 nets for each value
I have posted zillions of examples in both the NEWSGROUP and ANSWERS. I use patternnet for classification.
Hope this helps.
Thank you for formally accepting my answer
Greg
