PyBrain simple regression example

Question

I am trying to do the simplest regression on pyBrain, but somehow I fail.

The neural network must study the function Y = 3 * X

from pybrain.supervised.trainers import BackpropTrainer from pybrain.datasets import SupervisedDataSet from pybrain.structure import FullConnection, FeedForwardNetwork, TanhLayer, LinearLayer, BiasUnit import matplotlib.pyplot as plt from numpy import * n = FeedForwardNetwork() n.addInputModule(LinearLayer(1, name = 'in')) n.addInputModule(BiasUnit(name = 'bias')) n.addModule(TanhLayer(1,name = 'tan')) n.addOutputModule(LinearLayer(1, name = 'out')) n.addConnection(FullConnection(n['bias'], n['tan'])) n.addConnection(FullConnection(n['in'], n['tan'])) n.addConnection(FullConnection(n['tan'], n['out'])) n.sortModules() # initialize the backprop trainer and train t = BackpropTrainer(n, learningrate = 0.1, momentum = 0.0, verbose = True) #DATASET DS = SupervisedDataSet( 1, 1 ) X = random.rand(100,1)*100 Y = X*3+random.rand(100,1)*5 for r in xrange(X.shape[0]): DS.appendLinked((X[r]),(Y[r])) t.trainOnDataset(DS, 200) plt.plot(X,Y,'.b') X=[[i] for i in arange(0,100,0.1)] Y=map(n.activate,X) plt.plot(X,Y,'-g') 

He does not know anything. I tried to remove the hidden layer (because in this example we don’t even need it), and the network began to predict NaN. What's happening?

EDIT: This is the code that solved my problem:

 #DATASET DS = SupervisedDataSet( 1, 1 ) X = random.rand(100,1)*100 Y = X*3+random.rand(100,1)*5 maxy = float(max(Y)) maxx = 100.0 for r in xrange(X.shape[0]): DS.appendLinked((X[r]/maxx),(Y[r]/maxy)) t.trainOnDataset(DS, 200) plt.plot(X,Y,'.b') X=[[i] for i in arange(0,100,0.1)] Y=map(lambda x: n.activate(array(x)/maxx)*maxy,X) plt.plot(X,Y,'-g') 

Answer 1

The main pybrain neurons are going to bring something between 0 and 1. Divide Y by 300 (the highest possible value) and you will get the best results.

More generally, find the maximum Y value for your dataset and scale it all out.