我知道这个问题已经过时了，但如果有人想做类似的事情，请扩展一下ahmedhosny's答案：

新的tensorflow数据集API能够使用python生成器创建数据集对象，因此除了scikit learn的KFold选项外，还可以使用KFold.split()生成器创建数据集：import numpy as np

from sklearn.model_selection import LeaveOneOut,KFold

import tensorflow as tf

import tensorflow.contrib.eager as tfe

tf.enable_eager_execution()

from sklearn.datasets import load_iris

data = load_iris()

X=data['data']

y=data['target']

def make_dataset(X_data,y_data,n_splits):

def gen():

for train_index, test_index in KFold(n_splits).split(X_data):

X_train, X_test = X_data[train_index], X_data[test_index]

y_train, y_test = y_data[train_index], y_data[test_index]

yield X_train,y_train,X_test,y_test

return tf.data.Dataset.from_generator(gen, (tf.float64,tf.float64,tf.float64,tf.float64))

dataset=make_dataset(X,y,10)

然后可以在基于图的tensorflow中或使用紧急执行来迭代数据集。使用紧急执行：for X_train,y_train,X_test,y_test in tfe.Iterator(dataset):

....