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Hi All, I try to test the neural network package AMORE, I normalized my data first, the input data is X [x1,x2,x3] where x1,x2,x3 each is 100 row 1 column vector. the output data Y is 100 row 1 column vector. my network has neurons=c(3,2,2,1) which 2 hidden layers, 3 node in the input layer while 1 in the output layer. Once the network is trained. I use sim (result$net, z) to

I am using the iris example came with nnet package to test AMORE. I can see the outcomes are similar to nnet with adaptative gradient descent. However, when I changed the method in the newff to the batch gradient descent, even by setting the epoch numbers very large, I still found all the iris expected class=2 being classified as class=3. In addition, all those records in the outcomes (y) are the three digits, 0, 0.4677313, and 0.5111955. The script is as below. Please help to understa...

...TRUE)) colnames(fitness2) <- c("Age","Weight","Oxygen","RunTime","RestPulse","RunPulse","MaxPulse") attach(fitness2) ## Create the input dataset indep <- fitness2[,-3] ## Create the neural network structure net.start <- newff(n.neurons=c(6,6,6,1),????? ???????????? learning.rate.global=1e-2,??????? ???????????? momentum.global=0.5,????????????? ???????????? error.criterium="LMS",?????????? ???????????? Stao=NA, hidden.layer="tansig",?? ???????????? output.layer="purelin",?????????? ??...