Building a neural net in memory for XOR problem using Joone
Code: "joone_XORMemory1.py"
Programming language: Python
DMelt Version 1.4. Last modified: 07/29/2017. License: Free
http://jwork.org/dmelt/code/cache/joone_XORMemory1_3164.py
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from org.joone.engine import NeuralNetListener
from org.joone.engine import SigmoidLayer,FullSynapse,Monitor 
from org.joone.io import MemoryInputSynapse
from org.joone.engine.learning import TeachingSynapse
from org.joone.net import NeuralNet
from java.lang import System

class joone(NeuralNetListener):

  mills=0
  err=-1

  def Go(self,epochs,inputArray):
     print "Running NN for ", epochs, " epochs"
     
     input=SigmoidLayer() # create 3 layers
     hidden=SigmoidLayer()
     output=SigmoidLayer()
     input.setLayerName("input")
     hidden.setLayerName("hidden")
     output.setLayerName("output")
     input.setRows(2);  hidden.setRows(3); output.setRows(1)
     synapse_IH = FullSynapse()  # input -> hidden conn. 
     synapse_HO = FullSynapse() # hidden -> output conn. 
     synapse_IH.setName("IH")
     synapse_HO.setName("HO")

     input.addOutputSynapse(synapse_IH) # Connect the input layer with the hidden layer
     hidden.addInputSynapse(synapse_IH)
     hidden.addOutputSynapse(synapse_HO) # Connect the hidden layer with the output layer
     output.addInputSynapse(synapse_HO)

     inputStream = MemoryInputSynapse()
     inputStream.setInputArray(inputArray)
     inputStream.setAdvancedColumnSelector("1,2") # The first two columns contain the input 

     input.addInputSynapse(inputStream) # set input data
     trainer = TeachingSynapse()
     samples = MemoryInputSynapse() # file containing the desired responses 

     samples.setInputArray(inputArray)  #The output values are on the third column of the file
     samples.setAdvancedColumnSelector("3")
     trainer.setDesired(samples)
     output.addOutputSynapse(trainer) # Connects the Teacher to the last layer 
     nnet = NeuralNet()                          # Creates a new NeuralNet 
     nnet.addLayer(input, NeuralNet.INPUT_LAYER) #  insert all layers 
     nnet.addLayer(hidden, NeuralNet.HIDDEN_LAYER)
     nnet.addLayer(output, NeuralNet.OUTPUT_LAYER)
     monitor = nnet.getMonitor()
     monitor.setTrainingPatterns(4)  # of rows (patterns) contained in the input file
     monitor.setTotCicles(epochs)    # How many times the net must be trained on the input patterns        
     monitor.setLearningRate(0.7)
     monitor.setMomentum(0.6)
     monitor.setLearning(True)                 #  The net must be trained
     monitor.setSingleThreadMode(True)  #  Set to false for multi-thread mode
     monitor.addNeuralNetListener(self)
     self.mills = System.currentTimeMillis()
     nnet.randomize(0.5)
     nnet.go(True) 

  def netStopped(self,e):
      delay = System.currentTimeMillis() - self.mills
      print "Training finished after ",delay," ms"
      mon = e.getSource() 
      self.err=mon.getGlobalError()
      
  def cicleTerminated(self,e): pass
      
  def netStarted(self,e): pass
     
  def errorChanged(self,e):
       mon = e.getSource() 
       c = mon.getCurrentCicle()
       cl = c / 1000
       if ((cl * 1000) == c): print c," cycles remaining - Error = ", mon.getGlobalError()

  def getError(self): return self.err

  def netStoppedError(self,e): pass 
       
inputArray=[[0.0, 0.0, 0.0],[0.0, 1.0, 1.0],[1.0, 0.0, 1.0],[1.0, 1.0, 0.0]]
xor=joone()
xor.Go(20000,inputArray)
print "Global RMSE after training=", xor.getError()