如何将python中的KNN Scikit-learn模型转换为tensorflow生活模型?

Question

这是我的准确率超过90%的KNN分类器的示例代码，

sc_X = StandardScaler()
X_train = sc_X.fit_transform(X_train)
X_test = sc_X.transform(X_test)
k=10
classifier=KNeighborsClassifier(n_neighbors=k)
classifier.fit(X_train,y_train)
y_pred=classifier.predict(X_test)
acc=accuracy_score(y_test, y_pred)
print("For K=",k,"-->Accuracy is:",acc) 

Am trying to convert the above listed model to a tensor flow lite model using this,
converter = lite.TFLiteConverter.from_keras_model(classifier)
tfmodel = converter.convert()
open('trained_model.tflite', 'wb').write(tfmodel)

但是我得到这个错误，

'KNeighborsClassifier'对象没有属性'call'

是否有方法将训练好的knn模型转换为tflite模型?

Answer 1

看起来KNeighborsClassifier是sklearn库的一部分。lite.TFLiteConverter.from_keras_model支持keras模型，不支持sklearn模型。你需要建立&训练一个Keras分类器