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Diffstat (limited to 'pybrain_experiments/classification_test.py')
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diff --git a/pybrain_experiments/classification_test.py b/pybrain_experiments/classification_test.py deleted file mode 100644 index ac5f272..0000000 --- a/pybrain_experiments/classification_test.py +++ /dev/null @@ -1,112 +0,0 @@ -from pybrain.datasets import ClassificationDataSet -from pybrain.utilities import percentError -from pybrain.tools.shortcuts import buildNetwork -from pybrain.supervised.trainers import BackpropTrainer -from pybrain.structure.modules import SoftmaxLayer - -from pylab import ion, ioff, figure, draw, contourf, clf, show, hold, plot -from scipy import diag, arange, meshgrid, where -from numpy.random import multivariate_normal - - -# To have a nice dataset for visualization, we produce a set of points in -# 2D belonging to three different classes. You could also read in your data -# from a file, e.g. using pylab.load(). - -means = [(-1,0),(2,4),(3,1)] -cov = [diag([1,1]), diag([0.5,1.2]), diag([1.5,0.7])] -alldata = ClassificationDataSet(2, 1, nb_classes=3) -for n in xrange(400): - for klass in range(3): - input = multivariate_normal(means[klass],cov[klass]) - alldata.addSample(input, [klass]) - - -# Randomly split the dataset into 75% training and 25% test data sets. -# Of course, we could also have created two different datasets to begin with. - -tstdata, trndata = alldata.splitWithProportion( 0.25 ) - - -# For neural network classification, it is highly advisable to encode -# classes with one output neuron per class. Note that this operation duplicates -# the original targets and stores them in an (integer) field named ‘class’. -trndata._convertToOneOfMany( ) -tstdata._convertToOneOfMany( ) - - -print "Number of training patterns: ", len(trndata) -print "Input and output dimensions: ", trndata.indim, trndata.outdim -print "First sample (input, target, class):" -print trndata['input'][0], trndata['target'][0], trndata['class'][0] - - - - - -# Now build a feed-forward network with 5 hidden units. We use the shortcut -# buildNetwork() for this. The input and output layer size must match the -# dataset’s input and target dimension. You could add additional hidden -# layers by inserting more numbers giving the desired layer sizes. -# -# The output layer uses a softmax function because we are doing classification. -# There are more options to explore here, e.g. try changing the hidden layer -# transfer function to linear instead of (the default) sigmoid. -# -# See also Description buildNetwork() for more info on options, and the Network -# tutorial Building Networks with Modules and Connections for info on how to -# build your own non-standard networks. -fnn = buildNetwork( trndata.indim, 5, trndata.outdim, outclass=SoftmaxLayer ) - - -# Set up a trainer that basically takes the network and training dataset -# as input. For a list of trainers, see trainers. We are using a -# BackpropTrainer for this. - -trainer = BackpropTrainer( fnn, dataset=trndata, momentum=0.1, - verbose=True, weightdecay=0.01) - - -# Now generate a square grid of data points and put it into a dataset, -# which we can then classify to obtain a nice contour field for visualization. -# Therefore the target values for this data set can be ignored. - -ticks = arange(-3.,6.,0.2) -X, Y = meshgrid(ticks, ticks) -# need column vectors in dataset, not arrays -griddata = ClassificationDataSet(2,1, nb_classes=3) -for i in xrange(X.size): - griddata.addSample([X.ravel()[i],Y.ravel()[i]], [0]) -griddata._convertToOneOfMany() # this is still needed to make the fnn feel comfy - - -for i in range(20): -# Train the network for some epochs. Usually you would -# set something like 5 here, but for visualization purposes we -# do this one epoch at a time. - trainer.trainEpochs( 1 ) - trnresult = percentError( trainer.testOnClassData(), - trndata['class'] ) - tstresult = percentError( trainer.testOnClassData( - dataset=tstdata ), tstdata['class'] ) - - print "epoch: %4d" % trainer.totalepochs, \ - " train error: %5.2f%%" % trnresult, \ - " test error: %5.2f%%" % tstresult - out = fnn.activateOnDataset(griddata) - out = out.argmax(axis=1) # the highest output activation gives the class - out = out.reshape(X.shape) - figure(1) - ioff() # interactive graphics off - clf() # clear the plot - hold(True) # overplot on - for c in [0,1,2]: - here, _ = where(tstdata['class']==c) - plot(tstdata['input'][here,0],tstdata['input'][here,1],'o') - if out.max()!=out.min(): # safety check against flat field - contourf(X, Y, out) # plot the contour - ion() # interactive graphics on - draw() # update the plot - -ioff() -show() |