How To Create Simple 3-layer Neural Network And Teach It Using Supervised Learning?

Based on PyBrain's tutorials I managed to knock together the following code: #!/usr/bin/env python2 # coding: utf-8 from pybrain.structure import FeedForwardNetwork, LinearLayer,

Solution 1:

There are four problems with your approach, all easy to identify after reading Neural Network FAQ:

• Why use a bias/threshold?: you should add a bias node. Lack of bias makes the learning very limited: the separating hyperplane represented by the network can only pass through the origin. With the bias node, it can move freely and fit the data better:

  bias = BiasUnit()
  n.addModule(bias)
  
  bias_to_hidden = FullConnection(bias, hiddenLayer)
  n.addConnection(bias_to_hidden)
  

• Why not code binary inputs as 0 and 1?: all your samples lay in a single quadrant of the sample space. Move them to be scattered around the origin:

  ds = SupervisedDataSet(2, 1)
  ds.addSample((-1, -1), (0,))
  ds.addSample((-1, 1), (1,))
  ds.addSample((1, -1), (1,))
  ds.addSample((1, 1), (0,))
  

• trainUntilConvergence method works using validation, and does something that resembles the early stopping method. This doesn't make sense for such a small dataset. Use trainEpochs instead. 1000 epochs is more than enough for this problem for the network to learn:

  trainer.trainEpochs(1000)
  

• What learning rate should be used for backprop?: Tune the learning rate parameter. This is something you do every time you employ a neural network. In this case, the value 0.1 or even 0.2 dramatically increases the learning speed:

  trainer = BackpropTrainer(n, dataset=ds, learningrate=0.1, verbose=True)
  

With these fixes I get consistent, and correct results for the given network with the given dataset, and error less than 1e-23.