def createMLPsH(self, H1, nu, mu, batchsize, k): for j in range(10) : data = Data(k, 0, 0) data.importDataFromMat() data.normalize() train = TrainerValidator(k, 5, H1, (j+1)*10, nu, mu, batchsize, data) train.trainAndClassify() train.plotResults()
def createMLPsP(self, H1, H2, nu, batchsize, k): for j in range(4,8) : data = Data(k, 0, 0) data.importDataFromMat() data.normalize() train = TrainerValidator(k, 50, H1, H2, nu, j/10.0, batchsize, data) train.trainAndClassify() train.plotResults()
def compareParameters():
k = 5
data = Data(k, 0, 0)
data.importDataFromMat()
data.normalize()
train = TrainerValidator(k, 40, 80, 60, 0.001, 0.1, 1, data)
train.trainAndClassify()
train2 = TrainerValidator(k, 40, 80, 60, 0.04, 0.1, 1, data)
train2.trainAndClassify()
train3 = TrainerValidator(k, 40, 80, 60, 0.1, 0.1, 1, data)
train3.trainAndClassify()
error_fig = plt.figure()
ax1 = error_fig.add_subplot(111)
ax1.plot(train.validation_error, label="validation error mu=0.1 nu=0.001")
ax1.plot(train.training_error, label="training error mu=0.1 nu=0.001")
ax1.plot(train2.validation_error, label="validation error mu=0.1 nu=0.04")
ax1.plot(train2.training_error, label="training error mu=0.1 nu=0.04")
ax1.plot(train3.validation_error, label="validation error mu=0.1 nu=0.1")
ax1.plot(train3.training_error, label="training error mu=0.1 nu=0.1")
ax1.set_ylabel("error")
ax1.set_xlabel("epoch")
title = "Validation and training errors k=5 H1=80 H2=60 batchsize=1"
error_fig.suptitle(title)
plt.legend()
def testBinary():
k = 2
data = Data(k, 0, 0)
data.importDataFromMat()
data.normalize()
train = TrainerValidator(k, 70, 100, 10, 0.1, 0.2, 1, data)
train.trainAndClassify()
train.plotResults()
test = Test(train.getMLP(), data, k)
test.classify()
test.examples()
test.plot_confusion_matrix()
