Regression function of Neural Networks
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I wrote a code for neural network for my project but, i could not find the regression function as a result. My code is;
inputs = initial1';
targets = output';
hiddenLayerSize = 6;
net = fitnet(hiddenLayerSize);
net.inputs{1}.processFcns = {'removeconstantrows','mapminmax'};
net.outputs{2}.processFcns = {'removeconstantrows','mapminmax'};
net.divideFcn = 'dividerand';
net.divideMode = 'sample';
samplenet.divideParam.trainRatio = 80/100;
net.divideParam.valRatio = 15/100;
net.divideParam.testRatio = 5/100;
net.trainFcn = 'trainbr'; % Bayesian regularization
net.performFcn = 'mse'; % Mean squared error
net.plotFcns = {'plotperform','plottrainstate','ploterrhist', ...
'plotregression', 'plotfit'};
[net,tr] = train(net,inputs,targets);
outputs = net(inputs);
errors = gsubtract(targets,outputs);
performance = perform(net,targets,outputs)
- My network is running without an error. but Could not find the regression of the variables.
5 个评论
Greg Heath
2012-5-13
I still do not know what you mean.
Are you looking for the mathematical equation that produces the same output as the net?
Greg
采纳的回答
Greg Heath
2012-5-15
In general, there is no way to get "the function for each variable".
If you vary one variable with all of the other variables fixed, the result depends on the particular combination of the fixed values.
There are N combinations of I-dimensional input data. If you take each input vector, hold I-1 variables fixed and vary the remaining one over it's range, you would get N different functions for that single variable. Plotting those N functions on one plot would probably not yield enough visual information to make it worthwhile. Doing this for each variable would probably not be very enlightning.
However, there are ways to estimate the relative importance of each variable. For example, you can scramble the N values of a single variable and record the resulting error. Repeat this a number (10?,20?,30?) of times and record the summary statistics (e.g., min/median/mean/std/max) of the MSE.
The ranking of the I means and medians of the variables should yield a reasonable understanding of the importance of each variable.Hope this helps.
Greg
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更多回答(2 个)
Ketan
2012-5-12
You can view the general structure of your network with the VIEW function:
view(net);
The IW, LW, and b Network properties store the weights and biases.
Greg Heath
2012-5-13
See my answer in the recent Answers post titled:
Write code for NN using the Weight and Bias data retrieved from the NN tool box
Hope this helps.
Greg
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