def train(self, args):
if(self.data.ds == None):
print("Can't train without loaded data")
return
if(args != [] and len(args) >= 2):
self.net.epochs = int(args[1])
if(self.net.trainingType == "gradient"):
if(self.trainer == None):
self.trainer, self.returnsNet = self.__getGradientTrainer();
self.__train(self.trainer.trainEpochs, self.returnsNet)
elif(self.net.trainingType == "optimization"):
if(self.trainer == None):
self.trainer, self.returnsNet = self.__getOptimizationTrainer();
self.__train(self.trainer.learn, self.returnsNet)
return
elif(self.trainingType == "crossval"):
if(self.trainer == None):
self.trainer, self.returnsNet = self.__getGradientTrainer();
evaluation = ModuleValidator.classificationPerformance(self.trainer.module, self.data.ds)
validator = CrossValidator(trainer=self.trainer, dataset=self.trainer.ds, n_folds=5, valfunc=evaluation, verbose=True, max_epochs=1)
print(validator.validate())
else:
raise Exception("Cannot create trainer, no network type specified" + self.trainingType)
hidden_layer_size=200
net = buildNetwork( t-1 , hidden_layer_size, 1 ,bias = True )
trainer = BackpropTrainer( module=net, dataset=ds1, momentum=0.1, verbose=True, weightdecay=0.01)
for i in range(20):
print("Training Epoch #"+str(i))
trainer.trainEpochs( 1 )
p = net.activateOnDataset(ds2)
p = p.argmax(axis=1) # the highest output activation gives the class
p = p.reshape(-1,-1)
print p
hitrate = ModuleValidator.classificationPerformance(module=net,dataset=ds1)
print hitrate
#trainer.trainUntilConvergence( verbose = True, validationProportion = 0.15, maxEpochs = 20)
# for i in range(20):
# trainer.trainEpochs(3)
# trnresult = percentError(trainer.testOnClassData(),y_train)
# tstresult = percentError(trainer.testOnClassData(dataset=ds2),y_test)
# print "epoch: %4d" % trainer.totalepochs, \
# "train error: %5.2f%%" % trnresult, \
# "test error: %5.2f%%" % tstresult
# out = net.activateOnDataset(ds1)
# out = out.argmax(axis=1) # the highest output activation gives the class
# #print out
# figure(1)
