Traing dataset,validation dataset,testing dataset.
xing...@gmail.com
i use three-fold cross validation way to divert training dataset
into training dataset and validation dataset. During the training,i
use early stopping by validation dataset.Then i calculate average MSE
and choose the structure of neural network.But i donot know how to
determine weights of neural network and get final neural network
model which i can use test dataset to evaluate the neural network
model.
Please help me!
Greg Heath
> First,i divert
Replace the term "divert" with "partition" (my preference) or
"split".
> dataset into training dataset and test dataset. Then
> i use three-fold cross validation way to divert training dataset
> into training dataset and validation dataset.
What you are describing below is not called 3-fold XVAL. The
proper terms are "Early Stopping" and "Stopped Training". See
the comp.ai.neural-nets FAQ. It also explains both f-fold and
leave-v-out cross-validation.
Search in Google Groups using
greg-heath XVAL
greg-heath cross-validation
for more details on cross-validation.
> use early stopping by validation dataset.Then i calculate average
> MSE
delete the adjective "average" ; the "M" in MSE already implies
averaging over the individual input vector squared errors.
Use the adjective "average" when you are averaging over the MSE
of different designs (e.g. in 10-fold XVAL).
> and choose the structure of neural network.
Do you mean number of hidden nodes, weights, or both?
> But i do not know how to
> determine weights of neural network and get final neural network
> model which i can use test dataset to evaluate the neural network
> model.
> Please help me!
Make multiple runs over (say) 10 to 30 different weight initializations
and choose the best design based on validation set error.
The test set is used for the final evaluation once the best design is
chosen.
If the test set results are unsatisfactory, the data set should be
repartitioned for a new design in order to make sure that the
new test set is independent of the new design.
Hope this helps.
Greg
Fernando Cunha
net.IW{1,1} to find out the weights from inlet to layer 1 (net is the name of the network you used)
then
net.LW{i,j} to find out the weights from layer j to layer i.
You can find the bias simply by using net.b(i), where i is the layer of interest.
I hope this could help you!
"Greg Heath" <he...@alumni.brown.edu> wrote in message <1156517720....@h48g2000cwc.googlegroups.com>...