我根据一门机器学习课程开发了一个基于人工神经网络（ANN）的模型，代码如下：

import numpy as npimport matplotlib.pyplot as pltimport pandas as pdimport tensorflow as tfdataset = pd.read_excel('CHURN DATA (2).xlsx')dataset.replace([np.inf, -np.inf], np.nan, inplace=True)dataset = dataset.fillna(0)X = dataset.iloc[:, 2:45].valuesy = dataset.iloc[:, 45].valuesfrom sklearn.preprocessing import LabelEncoderle = LabelEncoder()X[:, 1] = le.fit_transform(X[:,1])X[:, 2] = le.fit_transform(X[:,2])X[:, 3] = le.fit_transform(X[:,3])from sklearn.compose import ColumnTransformerfrom sklearn.preprocessing import OneHotEncoderct = ColumnTransformer(transformers=[('encoder', OneHotEncoder(),[0])], remainder = 'passthrough')X = np.array(ct.fit_transform(X))from sklearn.model_selection import train_test_splitX_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2)from sklearn.preprocessing import StandardScalersc = StandardScaler()X_train = sc.fit_transform(X_train)X_test = sc.transform(X_test)ann = tf.keras.models.Sequential()ann.add(tf.keras.layers.Dense(units = 43, activation = 'relu'))ann.add(tf.keras.layers.Dense(units = 43, activation = 'relu'))ann.add(tf.keras.layers.Dense(units = 1, activation = 'sigmoid'))ann.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])ann.fit(X_train, y_train, batch_size = 256, epochs = 100)y_pred = ann.predict(X_test)y_pred = (y_pred > 0.5)from sklearn.metrics import confusion_matrix, accuracy_scorecm = confusion_matrix(y_test, y_pred)print(cm)accuracy_score(y_test, y_pred)

然而，当我尝试添加K折交叉验证时，如下所示：

from sklearn.model_selection import cross_val_scoreaccuracies = cross_val_score(ann, X = X_train, y = y_train, cv = 10)mean = accuracies.mean()variance = accuracies.std()

我得到了以下错误：

TypeError: If no scoring is specified, the estimator passed should have a 'score' method. The estimator <tensorflow.python.keras.engine.sequential.Sequential object at 0x000001A52F049F88> does not.

当我尝试使用准确率作为评分标准时，如下所示：

accuracies = cross_val_score(estimator = ann,scoring = "accuracy", X = X_train, y = y_train, cv = 10)

我得到了以下错误：

Cannot clone object '<tensorflow.python.keras.engine.sequential.Sequential object at 0x000001A52F049F88>' (type <class 'tensorflow.python.keras.engine.sequential.Sequential'>): it does not seem to be a scikit-learn estimator as it does not implement a 'get_params' methods.

回答：

错误信息已经说明了一切。你不能直接将Keras模型传递给Sklearn。Keras为Sklearn提供了一个包装器，因此两者确实可以一起使用。它的名称是tensorflow.keras.wrappers.scikit_learn.KerasClassifier。

以下是使用MNIST数据集的可重现示例：

array([0.74008333, 0.65      , 0.71075   , 0.561     , 0.66683333])