I am trying to train a simple neural network with Pybrain. After training I want to confirm that the nn is working as intended, so I activate the same data that I used to train it with. However every activation outputs the same result. Am I misunderstanding a basic concept about neural networks or is this by design?
I have tried altering the number of hidden nodes, the hiddenclass type, the bias, the learningrate, the number of training epochs and the momentum to no avail.
This is my code...
from pybrain.tools.shortcuts import buildNetwork
from pybrain.datasets import SupervisedDataSet
from pybrain.supervised.trainers import BackpropTrainer
net = buildNetwork(2, 3, 1)
net.randomize()
ds = SupervisedDataSet(2, 1)
ds.addSample([77, 78], 77)
ds.addSample([78, 76], 76)
ds.addSample([76, 76], 75)
trainer = BackpropTrainer(net, ds)
for epoch in range(0, 1000):
error = trainer.train()
if error < 0.001:
break
print net.activate([77, 78])
print net.activate([78, 76])
print net.activate([76, 76])
This is an example of what the results can be... As you can see the output is the same even though the activation inputs are different.
[ 75.99893007]
[ 75.99893007]
[ 75.99893007]
