R help - May 2013 - Neural network: Amore adaptative vs batch why the results are so different?

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 understand this behavior.


library('AMORE')
ir <- rbind(iris3[,,1], iris3[,,2], iris3[,,3])
targets <- matrix(c(rep(c(1,0,0),50), rep(c(0,1,0),50), rep(c(0,0,1),50)),
150, 3, byrow=TRUE)
samp <- c(sample(1:50,25), sample(51:100,25), sample(101:150,25))
net <- newff(n.neurons=c(4, 2, 3), # number of units per layer
             learning.rate.global=1e-2,            # learning rate at which
every neuron is trained
             momentum.global=5e-4,                  # momentum for every
neuron
             error.criterium="LMS",                # error criterium:
least
mean squares
             hidden.layer="sigmoid",                # activation
function
of the hidden layer neurons
             output.layer="sigmoid",               # activation
function of
the output layer neurons
             method="BATCHgdwm")                   # training method:
adaptative or batch
nnfit <- train(net,                                   # network to train
            ir[samp,],                  # input training samples
            targets[samp,],       # output training samples
            error.criterium="LMS",                 # error criterium
            report=TRUE,                           # provide information
during training
            n.show=10,                              # number of times to
report
            show.step=40000)
y<-sim(nnfit$net,ir[samp,])

Thanks,
Xiaoyan

	[[alternative HTML version deleted]]