neural network with bayesian regularization: find weights and biases and recalculate the network
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Michael Arnold
2020 年 12 月 11 日
編集済み: Michael Arnold
2020 年 12 月 15 日
Hey,
i´m trying to use a neural network to guess functional values for unknown points. This is my current solution.
%target f(x)=(x^2 + 22*x - 100)/(4*x)
%for x = [2,9]
inputall = 2:0.01:9;
outputall = (inputall.^2+22*inputall-100)./(4*inputall);
%training data
inputtrain = 2:1:9;
outputtrain = (inputtrain.^2+22*inputtrain-100)./(4*inputtrain);
%neural network
neurons = 5;
net = feedforwardnet(neurons,'trainbr');
net = train(net,inputtrain,outputtrain);
%prediction
predict(1,:) = net(inputall);
%comparison
comp = [outputall' predict']
%visualization
figure('Name','comparison'); hold on;
plot(inputall,outputall);
plot(inputall,predict)
Now I want to know what weights and biases the network finaly used. How can i get them and is it possible to use them to recalculate by myself the solution of the network?
Best regards
Michael
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Sai Veeramachaneni
2020 年 12 月 15 日
Hi,
You can use net.IW, net.LW, net.b properties of neural network object to get weights and biases used in the network.
References:
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