#Target dimension is supposed to be 1
#The targets are class labels starting from zero
for i in range(N):
alldata.appendLinked(Xdf.ix[i,:],Ydf['default_Yes'].ix[i,:])
#generate training and testing data sets
tstdata, trndata = alldata.splitWithProportion(0.10)
#classes are encoded into one output unit per class, that takes on a certain value if the class is present
trndata._convertToOneOfMany( )
tstdata._convertToOneOfMany( )
len(tstdata), len(trndata)
#calculate statistics and generate histograms
alldata.calculateStatistics()
print alldata.classHist
print alldata.nClasses
print alldata.getClass(1)
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#construct the network
from pybrain.structure import FeedForwardNetwork
net=FeedForwardNetwork()
#constructing the input, hidden and output layers
from pybrain.structure import LinearLayer, SigmoidLayer
inLayer = LinearLayer(3,name="input_nodes")
hiddenLayer1 = SigmoidLayer(3,name="hidden_nodes1")
hiddenLayer2 = SigmoidLayer(3,name="hidden_nodes1")
outLayer = LinearLayer(2,name="output_node")
avgluminosity = sum(pixels) / len(pixels)
processedpixels = map(
lambda p: min(p + 255 / 2 - avgluminosity, 255)
if (avgluminosity < 255 / 2) else max(p + 255 / 2 - avgluminosity, 0),
pixels)
# Save lowres images
a = np.array(processedpixels)
a = a.reshape(-1, width)
im = toimage(a)
im.save(os.path.join(masyafolder + '/bw', trainingpicture))
ds.appendLinked(processedpixels, [1]) # 0 = Murka; 1 = Masya
print "There are", ds.nClasses, "classes"
print "Class 0 is", ds.getClass(0)
tstdata_temp, trndata_temp = ds.splitWithProportion(0.25)
tstdata = ClassificationDataSet(height * width,
1,
nb_classes=2,
class_labels=['Murka', 'Masya'])
for n in xrange(0, tstdata_temp.getLength()):
tstdata.addSample(
tstdata_temp.getSample(n)[0],
tstdata_temp.getSample(n)[1])
trndata = ClassificationDataSet(height * width,
1,
nb_classes=2,
ds_training.appendLinked(image_vector, [category])
category+=1
category = 0
for shape in shapes:
for i in range(8):
image = imread('C:/Users/alexis.matelin/Documents/Neural Networks/Visual classification/shapes/testing/'+shape+str(i+1)+'.png', as_grey=True, plugin=None, flatten=None)
image_vector = image.flatten()
ds_testing.appendLinked(image_vector, [category])
ds_training.calculateStatistics()
ds_training.getClass(0)
print(ds_training.getField('target'))
ds_training._convertToOneOfMany(bounds=[0, 1])
ds_testing._convertToOneOfMany(bounds=[0, 1])
print(ds_training.getField('target'))
net = buildNetwork(1024,12, 12, 3, hiddenclass = TanhLayer, outclass=SoftmaxLayer)
trainer = BackpropTrainer(net, dataset=ds_training, verbose=True, learningrate=0.01)
trainer.trainUntilConvergence()
out = net.activateOnDataset(ds_testing)
def castToRegression(self, values):
"""Converts data set into a SupervisedDataSet for regression. Classes
are used as indices into the value array given."""
regDs = SupervisedDataSet(self.indim, 1)
fields = self.getFieldNames()
fields.remove('target')
for f in fields:
regDs.setField(f, self[f])
regDs.setField('target', values[self['class'].astype(int)])
return regDs
if __name__ == "__main__":
dataset = ClassificationDataSet(2, 1, class_labels=['Urd', 'Verdandi', 'Skuld'])
dataset.appendLinked([ 0.1, 0.5 ] , [0])
dataset.appendLinked([ 1.2, 1.2 ] , [1])
dataset.appendLinked([ 1.4, 1.6 ] , [1])
dataset.appendLinked([ 1.6, 1.8 ] , [1])
dataset.appendLinked([ 0.10, 0.80 ] , [2])
dataset.appendLinked([ 0.20, 0.90 ] , [2])
dataset.calculateStatistics()
print(("class histogram:", dataset.classHist))
print(("# of classes:", dataset.nClasses))
print(("class 1 is: ", dataset.getClass(1)))
print(("targets: ", dataset.getField('target')))
dataset._convertToOneOfMany(bounds=[0, 1])
print("converted targets: ")
print((dataset.getField('target')))
dataset._convertToClassNb()
print(("reconverted to original:", dataset.getField('target')))
