xor.py: more comments, more verbous output

xor.py

@@ -1,34 +1,43 @@
-#learn XOR with a nerual network with saving of the learned paramaters
+# train a neural network to do XOR 
+#
+# original code at https://github.com/EricSchles/neuralnet/blob/master/xor.py
+# by Eric Schles
 
 import pybrain
 from pybrain.datasets import *
 from pybrain.tools.shortcuts import buildNetwork
 from pybrain.supervised.trainers import BackpropTrainer
 import pickle
 
-if __name__ == "__main__":
-	ds = SupervisedDataSet(2, 1)
-	ds.addSample( (0,0) , (0,))
-	ds.addSample( (0,1) , (1,))
-	ds.addSample( (1,0) , (1,))
-	ds.addSample( (1,1) , (0,))
+# =============
+# the data we want to train with:
+# every sample contains the two inputs, and the one expected output
+# this represents the XOR operation:  only if the two inputs are different should the output be true=1
+print "setting up training data"
+ds = SupervisedDataSet(2, 1)
+ds.addSample((0,0), (0))
+ds.addSample((0,1), (1))
+ds.addSample((1,0), (1))
+ds.addSample((1,1), (0))
 
-	net = buildNetwork(2, 4, 1, bias=True)
+# =============
+# build up a neural network
+# the arguments are:  2=number of input nodes, 4=number of hidden nodes, 1=number of output nodes
+print "building the network"
+net = buildNetwork(2,4,1,bias=True)
 
-	# try:
-        #         f = open('_learned', 'r')
-	# 	net = pickle.load(f)
-	# 	f.close()
-	# except:
-        trainer = BackpropTrainer(net, learningrate = 0.01, momentum = 0.99)
-        trainer.trainOnDataset(ds, 3000)
-        trainer.testOnData()
-        # f = open('_learned', 'w')
-        # pickle.dump(net, f)
-        # f.close()
-
+# =============
+# do the training with the data:
+print "training the network"
+trainer = BackpropTrainer(net, learningrate = 0.01, momentum = 0.99)
+trainer.trainOnDataset(ds, 3000)
+trainer.testOnData()
 
-	print net.activate((1,1))
-        print net.activate((1,0))
-        print net.activate((0,1))
-        print net.activate((0,0))
+# =============
+# now we use the trainig data to test the network:
+# we loop through all the samples:
+print "checking the network"
+for inp, target in ds:
+  out = net.activate(inp)
+  print "Input %s: Output of Network is %f. Expected output is %f." % ( repr( inp ), out, target )
+print "done testing."