#!/usr/bin/env python # Example script for feed-forward network usage in PyBrain. __author__ = "Martin Felder" __version__ = '$Id$' from pylab import figure, ioff, clf, contourf, ion, draw, show from pybrain.utilities import percentError from pybrain.tools.shortcuts import buildNetwork from pybrain.supervised.trainers import BackpropTrainer from pybrain.structure.modules import SoftmaxLayer from datasets import generateGridData, generateClassificationData, plotData # load the training data set trndata = generateClassificationData(250) # neural networks work better if classes are encoded using # one output neuron per class trndata._convertToOneOfMany( bounds=[0,1] ) # same for the independent test data set tstdata = generateClassificationData(100) tstdata._convertToOneOfMany( bounds=[0,1] ) # build a feed-forward network with 20 hidden units, plus # a corresponding trainer fnn = buildNetwork( trndata.indim, 5, trndata.outdim, outclass=SoftmaxLayer ) trainer = BackpropTrainer( fnn, dataset=trndata, momentum=0.1, verbose=True, weightdecay=0.01) # generate a grid of data points for visualization griddata, X, Y = generateGridData([-3.,6.,0.2],[-3.,6.,0.2])
from pybrain.datasets import ClassificationDataSet
from pybrain.utilities import percentError
from pybrain.structure.modules.svmunit import SVMUnit
from pybrain.supervised.trainers.svmtrainer import SVMTrainer
# import some local stuff
from datasets import generateClassificationData, plotData, generateGridData
logging.basicConfig(level=logging.INFO,
filename=join('.', 'testrun.log'),
format='%(asctime)s %(levelname)s %(message)s')
logging.getLogger('').addHandler(logging.StreamHandler())
# load the training and test data sets
trndata = generateClassificationData(20, nClasses=2)
tstdata = generateClassificationData(100, nClasses=2)
# initialize the SVM module and a corresponding trainer
svm = SVMUnit()
trainer = SVMTrainer(svm, trndata)
# train the with fixed meta-parameters
log2C = 0. # degree of slack
log2g = 1.1 # width of RBF kernels
trainer.train(log2C=log2C, log2g=log2g)
# alternatively, could train the SVM using design-of-experiments grid search
##trainer.train( search="GridSearchDOE" )
# pass data sets through the SVM to get performance
trnresult = percentError(svm.activateOnDataset(trndata), trndata['target'])
#!/usr/bin/env python # Example script for feed-forward network usage in PyBrain. __author__ = "Martin Felder" __version__ = '$Id$' from pylab import figure, ioff, clf, contourf, ion, draw, show from pybrain.utilities import percentError from pybrain.tools.shortcuts import buildNetwork from pybrain.supervised.trainers import BackpropTrainer from pybrain.structure.modules import SoftmaxLayer from datasets import generateGridData, generateClassificationData, plotData # load the training data set trndata = generateClassificationData(250) # neural networks work better if classes are encoded using # one output neuron per class trndata._convertToOneOfMany( bounds=[0,1] ) # same for the independent test data set tstdata = generateClassificationData(100) tstdata._convertToOneOfMany( bounds=[0,1] ) # build a feed-forward network with 20 hidden units, plus # a corresponding trainer fnn = buildNetwork( trndata.indim, 5, trndata.outdim, outclass=SoftmaxLayer ) trainer = BackpropTrainer( fnn, dataset=trndata, momentum=0.1, verbose=True, weightdecay=0.01) # generate a grid of data points for visualization griddata, X, Y = generateGridData([-3.,6.,0.2],[-3.,6.,0.2])
from pybrain.utilities import percentError
from pybrain.structure.modules.svmunit import SVMUnit
from pybrain.supervised.trainers.svmtrainer import SVMTrainer
# import some local stuff
from datasets import generateClassificationData, plotData, generateGridData
logging.basicConfig(
level=logging.INFO, filename=join(".", "testrun.log"), format="%(asctime)s %(levelname)s %(message)s"
)
logging.getLogger("").addHandler(logging.StreamHandler())
# load the training and test data sets
trndata = generateClassificationData(20, nClasses=2)
tstdata = generateClassificationData(100, nClasses=2)
for inpt, target in trndata:
print inpt, target
# initialize the SVM module and a corresponding trainer
svm = SVMUnit()
trainer = SVMTrainer(svm, trndata)
# train the with fixed meta-parameters
log2C = 0.0 # degree of slack
log2g = 1.1 # width of RBF kernels
trainer.train(log2C=log2C, log2g=log2g)
# alternatively, could train the SVM using design-of-experiments grid search
##trainer.train( search="GridSearchDOE" )
