I'm using the neuralnet in R to build a NN with 14 inputs and one output. I build/train the network several times using the same input training data and the same network architecture/settings.
After each network is produced I use it on a stand alone set of test data to calculate some predicted values. I'm finding there is a large variance in each iteration of the predicted data, despite all the inputs (both the training data and test data) remaining the same each time I build the network.
I understand that there will be differences in the weightings produced within the NN each time and that no two neural networks will be identical, but what can I try to produce networks that are more consistent across each train, given the identical data?