from keras.wrappers.scikit_learn import KerasClassifier    from sklearn.model_selection import cross_val_score    def build_classifier():        classifier = Sequential()        classifier.add(Dense(units = 6, activation = 'relu', input_dim = 11, kernel_initializer =         'uniform'))        classifier.add(Dense(units = 6, activation = 'relu', kernel_initializer = 'uniform'))        classifier.add(Dense(units = 1, activation = 'sigmoid', kernel_initializer = 'uniform'))        classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])        return classifier    classifier = KerasClassifier(build_fn = build_classifier, batch_size = 10, epochs = 100)    accuracies = cross_val_score(estimator = classifier, X = X_train, y = y_train, cv = 10, n_jobs = -1)

/这段代码看起来很干净，但K折交叉验证似乎不起作用。它返回NaN值，并且执行速度很快，与使用所有CPU时的表现相反/

回答：

您定义的分类器函数可能存在错误，导致输出不正确。我猜测您应该检查输入层中输入节点的数量是否定义得当，因为您的其他代码看起来没有问题。