Finding best neural network structure using optimization algorithms and cross-validation


I'm using optimization algorithm to find best structure+inputs of a 'patternnet' neural network in MATLAB R2014a using 5-fold cross validation. Where should i initialize weights of my neural network?
 *Position_1(for weight initialization)*

 for i=1:num_of_loops
 *Position_2(for weight initialization)* 

 - repeating cross validation
 for i=1:num_of_kfolds
 *Position_3(for weight initialization)*
 - Cross validation loop

I'm repeating 5-fold cross validation (because random selection of cross validation) to have more reliable outputs (average of neural network outputs). Which part is better for weight initialization (Position_1,Position_2 or Position_3) and why?

ANSWER provide latest MatLab Homework Help,MatLab Assignment Help for students, engineers and researchers in Multiple Branches like ECE, EEE, CSE, Mechanical, Civil with 100% output.Matlab Code for B.E, B.Tech,M.E,M.Tech, Ph.D. Scholars with 100% privacy guaranteed. Get MATLAB projects with source code for your learning and research.

To help understanding, I will assume Nval = Ntst = 0. Search for the nonzero examples in the NEWSGROUP and ANSWERS.
To design a typical I-H-O net with Ntrn training examples, try to not let the number of unknown weights
 Nw = (I+1)*H+(H+1)*O

exceed the number of training equations

 Ntrneq = Ntrn*O

This will occur as long as H <= Hub where Hub is the upperbound

 Hub = -1+ceil( (Ntrneq-O) / (I+O+1) )

Based on Ntrneq and Hub I decide on a set of numH candidate values for H

 0 <= Hmin:dH:Hmax <= Hmax

 numH = numel(Hmin:dH:Hmax)

and the number of weight initializations for each value of H, e.g.,



Popular posts from this blog

Why are Fourier series important? Are there any real life applications of Fourier series?

What are some good alternatives to Simulink?

What is the difference between a reversible machine and a self-locking machine?