Neural Networks regression using adapt
3 views (last 30 days)
Show older comments
Hi,
I have been working on writing different matlab scripts to perform regression using several built-in MATLAB algorithms, such as boosted regression trees, bagged decision trees and neural networks. Because my data set is relatively very large and it takes a good amount of time to train it, I decided trying to use incremental learning and compare the differences between that and training the whole data I have. My question is: the MSE and R^2 when I use adapt are considerably worse compared to when I train the whole training data normally. I was wondering if it is actually supposed to be this way or if there is something wrong with my implementation. Could anyone help me? Here is the code:
% Initializations
clear all;
close all;
clear classes;
trainfraction = 1/7; % e.g. 10 percent
% trainArr will be used for adapt. My training data is separated into years. I will call train only
% over the 2003 data, use adapt for data from 2004 and 2010 and use them to predict data from 2011
% loadData simply puts the .mat into feature matrices and output vectors.
trainArr = {'Data2004.mat', 'Data2005.mat', 'Data2006.mat', 'Data2007.mat', 'Data2008.mat', 'Data2009.mat', 'Data2010.mat' };
[X_Train,Y_Train,T_Train] = loadData({'Data2003.mat'} , trainfraction);
[X_PredYear,Y_PredYear,T_PredYear] = loadData('Data2011.mat', 0);
%%Neural Network
input = X_Train';
output = Y_Train';
net1 = feedforwardnet([20 20]);
net1.trainFcn = 'trainscg';
net1.trainParam.max_fail = 100;
net1 = train(net1, input, output);
y = net1(input);
% Predict
input = X_PredYear';
output = Y_PredYear';
input_test = input;
target_test = output;
predict = sim(net1,input_test);
% Performance
performance = mse(net1, target_test, predict)
%%R Squared
target_test_mean = mean(target_test);
SStot = sum((target_test - target_test_mean).^2);
SSreg = sum((predict - target_test_mean).^2);
SSres = sum((target_test - predict).^2);
Rerr = 1-SSres/SStot
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Here I will use the adapt to increment the learning I got from "train"
for i = 1:numel(trainArr)
[X_Train,Y_Train,T_Train] = loadData(trainArr(i) , trainfraction);
input = X_Train';
output = Y_Train';
net1 = adapt(net1, input, output);
y = net1(input);
predict = sim(net1,input_test);
% Performance
performance = perform(net1, target_test, predict)
%%R Square
target_test_mean = mean(target_test);
SStot = sum((target_test - target_test_mean).^2);
SSreg = sum((predict - target_test_mean).^2);
SSres = sum((target_test - predict).^2);
Rerr = 1-SSres/SStot
end
For the results, when I use train for all training data my MSE is a bit above 0.3 and R^2 around 0.76. Using adapt, after the last call to adapt my MSE is 0.46 and R^2 around 0.66. Any ideas?
1 Comment
Greg Heath
on 25 Oct 2015
How many random initial weight trials did you run for each case?
Can you demonstrate this using a MATLAB example dataset?
help nndatasets
doc nndatasets
Greg
Answers (0)
See Also
Categories
Find more on Sequence and Numeric Feature Data Workflows in Help Center and File Exchange
Products
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!