I've tried to reproduce the results I got from a trained network but was unable. Here's my script:
load 'C:\Users\ZA\Documents\cosjacorr.mat' inputs = cosjacorr(:, 1:9)'; targets = cosjacorr(:, 10:13)'; numHiddenNeurons = 2; % Adjust as desired net = newpr(inputs,targets,numHiddenNeurons); net.divideParam.trainRatio = 75/100; % Adjust as desired net.divideParam.valRatio = 15/100; % Adjust as desired net.divideParam.testRatio = 10/100; % Adjust as desired net.inputs{1}.processFcns = {}; net.outputs{2}.processFcns = {}; % Train and Apply Network [net,tr] = train(net,inputs,targets); outputs = sim(net,inputs); % Plot plotconfusion(targets,outputs)
I removed all pre and post processing hoping that I would be able to reproduce the results but was unsuccessful.
Here's what I did in terms of replicating the network's output:
y1 = tansig(net.IW{1} * input + net.b{1}); Results = tansig(net.LW{2} * y1 + net.b{2});
ANSWER
Matlabsolutions.com 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.
Type "whos" to see the dimensionality of all variables and parameters.
% help newpr % doc newpr % Obsoleted in R2010b NNET 7.0. Last used in R2010a close all, clear all, clc [x t ] = simpleclass_dataset; [ I N ] = size(x) % [ 2 1000 ] [O N ] = size(t) % [ 4 1000] H = 2 net = newpr(x,t,H); net = train(net,x,t); y = net(x); IW = net.IW{1,1}; b1 = net.b{1}; b2 = net.b{2}; LW = net.LW{2,1};
SEE COMPLETE ANSWER CLICK THE LINK
Comments
Post a Comment