I have successfully implemented ANN architecture for one output problem. Now I would like to add more than one output vectors in the model, meaning [X, Y] = f[A, B, C ....]. I have been using R to do this but struggling with multi-output problems. My specific questions are:
How do I add more than one output in the code?
Does the activation function (using sigmoid) have a relation with the number of vector outputs in the model?
Any help would be appreciated.
Thanks!
Hi,
Use this code by specifying your inputs and outputs first using load '' command .
They must be line vectors.
x='input';
y='output';
p=x;
t=y;
net2=newff(p,t,15,{'tansig','purelin'},'trainlm');
net2.trainParam.epochs=500;
net2=init(net2);
net2.trainparam.lr=0.25;
net2.divideParam.trainRatio=.7;
net2.divideParam.valRatio=.30;
net2.divideParam.testRatio=0;
net2 = train(net2,p,t);
train = sim(net2,p);
test = sim(net2,testing_data_input');
Keep me informed of your progress to see how I can help you.
Best regards.
Read this research I think contains all your questions in this direction ((Multiple-input multiple-output vs. single-input single-output neural network forecasting”))
The number of outputs of a neural network depend on the architecture (the number of nodes and layers of nodes connecting the input to the output) you choose and is not (necessarily) related to the activation function (although the activation function is important for the performance of the network).
Usually, when we want more than a single numeric output we add more nodes (and possibly more layers of nodes) and bundle its outputs together and then reshape it according to their meaning, so they form a vector, a matrix, a set of vectors, a tensor of higher rank and so on.
Now, I don't know if R does proper machine learning. To train a neural network with more than one note we have to resort to backpropagation (which I believe to be your main problem in this question), which can be fairly annoying (and sometimes difficult) to implement by hand for a given architecture. That said, there are plenty of programming packages that implement efficient backpropagation for the architecture you choose, the most popular being Google's TensorFlow, for which there is, probably, a R bindings package.
It can be done using 'neuralnet' package. You can visit the following link for such example.
https://www.google.com/amp/s/www.r-bloggers.com/multilabel-classification-with-neuralnet-package/amp/
Here is a simple example code for classification problem with IRIS dataset
library(neuralnet)
library(nnet) #This is for 'class.ind' function
data("iris")
trainData
Hi I made this code for my research I hope it will help you
I use 8 hidden layer and 2 output
P=[input]
T=[output1 ; output 2]
net=newff(p,t,[8 2],{'tansig','purelin'},'trainlm');
learparam.Ir=.75;
net.trainParam.mc=.9;
net.trainParam.show = 5;
net.trainParam.epochs = 1200;
net.trainParam.goal = 1e-4;
[net,tr]=train(net,p,t);
plot(tr.epoch, tr.perf, tr.epoch, tr.vperf, tr.epoch, tr.tperf);
legend('Training', 'Validation', 'Test', -1);
ylabel('Squared Error'); xlabel('Epoch');
a=sim(net,p)
weights1=net.iw{1,1}
b=net.b{1}
weights2=net.lw{2,1}
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