Function svc

DeepLearning Tutorials/dive_into_keras/cnn-svm.py:17–25

(traindata,trainlabel,testdata,testlabel)

def svc(traindata,trainlabel,testdata,testlabel):
    print("Start training SVM...")
    svcClf = SVC(C=1.0,kernel="rbf",cache_size=3000)
    svcClf.fit(traindata,trainlabel)
    
    pred_testlabel = svcClf.predict(testdata)
    num = len(pred_testlabel)
    accuracy = len([1 for i in range(num) if testlabel[i]==pred_testlabel[i]])/float(num)
    print("cnn-svm Accuracy:",accuracy)

def rf(traindata,trainlabel,testdata,testlabel):
    print("Start training Random Forest...")