ValueError：发现样本数量不一致的输入变量：[218, 30]

Question

我正在使用线性 SVC 进行一些面部识别训练，我的数据集是 870x22。我有 29 个不同的人的 30 帧，我在图像中使用 22 个简单值像素来识别面部图像，说 22 个像素是我的特征。此外，当我调用 train_test_split() 时，它会给我一个大小为 218x22 的 X_test 和大小为 218 的 y_test。一旦我训练了分类器并尝试运行新面孔 (30x22) 矩阵的图像，它就会给我错误：

ValueError: Found input variables with inconsistent numbers of samples: [218, 30]

这是代码：

import sklearn

from sklearn.model_selection import train_test_split

from sklearn import metrics

from sklearn.svm import SVC

from sklearn.metrics import confusion_matrix

from sklearn.metrics import accuracy_score, f1_score

    img_amount = 30

    target = np.asarray([1]*img_amount + [2]*img_amount + [3]*img_amount + [4]*img_amount + [5]*img_amount + [6]*img_amount + [7]*img_amount + [8]*img_amount + [9]*img_amount + [10]*img_amount + [11]*img_amount + [12]*img_amount + [13]*img_amount + [14]*img_amount + [15]*img_amount + [16]*img_amount + [17]*img_amount + [18]*img_amount + [19]*img_amount + [20]*img_amount + [21]*img_amount + [22]*img_amount + [23]*img_amount + [24]*img_amount + [25]*img_amount + [26]*img_amount + [27]*img_amount + [28]*img_amount + [29]*img_amount)   

    dataset= dataset[:, 0:22]

        

        svc_1 = SVC(kernel='linear', C=0.00005)

        X_train, X_test, y_train, y_test = train_test_split( dataset, target, test_size=0.25, random_state=0)

        

        def train(clf, X_train, X_test, y_train, y_test):

            

            clf.fit(X_train, y_train)

            print ("Accuracy on training set:")

            print (clf.score(X_train, y_train))

            print ("Accuracy on testing set:")

            print (clf.score(X_test, y_test))

            

            y_pred = clf.predict(X_test)

            

            print ("Classification Report:")

            print (metrics.classification_report(y_test, y_pred))

            print ("Confusion Matrix:")

            print (metrics.confusion_matrix(y_test, y_pred))

Answer 1

这是问题所在：predictions = svc_1.predict(new_face_image) print ("Confusion Matrix:")print (metrics.confusion_matrix(y_test, predictions))您正在预测 new_face_image 并使用您的测试数据集对其进行预测。predictions = svc_1.predict(new_face_image) # change this to what you expect but shape=(30,)expected=np.ones(len(new_face_image))print ("Confusion Matrix:")print (metrics.confusion_matrix(expected, predictions))编辑以使用数据集测试数据进行验证：predictions = svc_1.predict(x_test) print ("Confusion Matrix:")print (metrics.confusion_matrix(y_test, predictions))