Neural Networks

What is the problem exactly? With regression in Tensorflow for example, it is common to run several loops and average the results. You often get much better results than if you tried to remove all the randomness. Embrace the randomness as they say. The problem is time as usual. Rapidminer has ensemble tools for grouping models but simple averaging goes long way and is also easy to understand. 

@hughesfleming68

Hello

Thank you for the answer. I have 9 different big data set and I have to use 9 networks for them then the result of all of them should import to an other network. The question is that is RM able to do this?( this is a new project and I try to make data set from long time ago and I should try it first then I should use python for that, so if I can use RM for that I can test it easily)

Also ensemble learning is not useful here.

Thank you
mbs 

Hi @mbs, which neural net operator are you using?

@hughesfleming68

Two of them:
1.Deep learning
2.Neural network

I try both of them.

Thank you for the answer
mbs

I guess if we are talking about example sets then the last NN is making a prediction of a prediction from the nine inputs sets. If it is models then one way or another this is still some sort of stacking problem. It is quite hard to visualise what you are trying to do. Maybe sketch out a diagram or perhaps someone else can jump in if I have not quite understood the problem. @varunm1 feel free anytime.

what is your idea to create an operator for that or write a library in R then use it?
( Data is a bit complex )

@hughesfleming68

Hello

Thank you for your answer. finally I find 3 ways to solve it

best
mbs

@mbs, I am glad that you did. Were your three options equally as good or was one way much better than the others? 

regards,

Alex

@hughesfleming68

Three different ways with training part and handling data or make one new library in R. All of them are useful.

Thank you for the time that you spend on my question
mbs