def train(self, event):
if self.train_f_name_tb.GetValue() != '':
ffp = FaceFileParser()
ffp.add_dir(self.train_f_name_tb.GetValue())
datalist = ffp.get_data()
layers = int(self.train_numLayers_tb.GetValue())
nodes = int(self.train_numNodes_tb.GetValue())
epochs = int(self.train_numEpochs_tb.GetValue())
nettype = self.netbox.GetSelection()
print layers
print nodes
print epochs
print nettype
if nettype == 0:
fc = SupervisedFacialClassifier()
if layers == 1:
errorData = fc.alternateTrain(datalist, [(nodes)], epochs,
5)
elif layers == 2:
errorData = fc.alternateTrain(datalist, (nodes, nodes),
epochs, 5)
elif layers == 3:
errorData = fc.alternateTrain(datalist,
(nodes, nodes, nodes),
epochs, 5)
print 'ErrorData:\n'
print errorData
print '\n'
for entry in errorData['training_error']:
print entry
else:
fc = UnsupervisedFacialClassifier(2, 2)
errorData = fc.train(datalist)
def train(self, event):
if self.train_f_name_tb.GetValue() != '':
ffp = FaceFileParser()
ffp.add_dir(self.train_f_name_tb.GetValue())
datalist = ffp.get_data()
layers = int(self.train_numLayers_tb.GetValue())
nodes = int(self.train_numNodes_tb.GetValue())
epochs = int(self.train_numEpochs_tb.GetValue())
nettype = self.netbox.GetSelection()
print layers
print nodes
print epochs
print nettype
if nettype == 0:
fc = SupervisedFacialClassifier()
if layers == 1:
errorData = fc.alternateTrain(datalist, [(nodes)], epochs, 5)
elif layers == 2:
errorData = fc.alternateTrain(datalist, (nodes,nodes), epochs, 5)
elif layers == 3:
errorData = fc.alternateTrain(datalist, (nodes,nodes,nodes), epochs, 5)
print 'ErrorData:\n'
print errorData
print '\n'
for entry in errorData['training_error']:
print entry
else:
fc = UnsupervisedFacialClassifier(2,2)
errorData = fc.train(datalist)
"""
print "Single Hidden Layer"
for i in range(2,20):
fc = FacialClassifier()
fc.alternateTrain(datalist, [(i)], 25)
print "Double Hidden Layer"
for i in range(2,20):
fc = FacialClassifier()
fc.alternateTrain(datalist, (i,i), 25)
print "Triple Hidden Layer"
for i in range(2,20):
fc = FacialClassifier()
fc.alternateTrain(datalist, (i,i,i), 25)
"""
fc = SupervisedFacialClassifier()
# alternateTrain(datalist, hiddenNodes, #epochs, log stats every X epochs)
fc.alternateTrain(datalist, (20, 20), 500, 10)
"""
#Test a single input file
ffp2 = FaceFileParser()
ffp2.add_file("c:\\face457b\\inputData\\in01.csv")
datalist2 = ffp2.get_data()
emo,data = datalist2[0]
fc.classify(data)
"""
"""
print "Single Hidden Layer"
for i in range(2,20):
fc = FacialClassifier()
fc.alternateTrain(datalist, [(i)], 25)
print "Double Hidden Layer"
for i in range(2,20):
fc = FacialClassifier()
fc.alternateTrain(datalist, (i,i), 25)
print "Triple Hidden Layer"
for i in range(2,20):
fc = FacialClassifier()
fc.alternateTrain(datalist, (i,i,i), 25)
"""
fc = SupervisedFacialClassifier()
# alternateTrain(datalist, hiddenNodes, #epochs, log stats every X epochs)
fc.alternateTrain(datalist, (20,20), 500, 10);
"""
#Test a single input file
ffp2 = FaceFileParser()
ffp2.add_file("c:\\face457b\\inputData\\in01.csv")
datalist2 = ffp2.get_data()
emo,data = datalist2[0]
fc.classify(data)
"""
from face_file_parser import FaceFileParser
from supervised_facial_classifier import SupervisedFacialClassifier
max_hn = 400
min_hn = 50
step_size = 25
epochs = 750
output = 50
ffp = FaceFileParser()
sfc = SupervisedFacialClassifier()
ffp.add_dir('./normData')
d_s = ffp.get_data()
csv_out = open('./vary_topology.csv','w')
csv_out.write('topology,min test error,at epoch,avg test error,avg train error\n')
l1 = min_hn
while l1 <= max_hn:
print 'Topology :' + str(l1) + '\n'
results = sfc.alternateTrain(d_s,[(l1)],epochs,logFreq=output)
min_err = min(results['testing_error'])
at_e = results['epochs'][results['testing_error'].index(min_err)]
avg_tst = results['avg_testing_error']
avg_trn = results['avg_training_error']
csv_out.write(str(l1) + ';' + ',' + str(min_err) + ','
+ str(at_e) + ',' + str(avg_tst) + ',' + str(avg_trn) + '\n')
print 'Minimum error of ' + str(min_err) + ' at epoch ' + str(at_e)
l2 = min_hn
while l2 <= max_hn:
