Does TensorFlow have cross validation implemented for its users?

As already discussed, tensorflow doesn't provide its own way to cross-validate the model. The recommended way is to use KFold. It's a bit tedious, but doable. Here's a complete example of cross-validating MNIST model with tensorflow and KFold:

from sklearn.model_selection import KFoldimport tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_data# Parameterslearning_rate = 0.01batch_size = 500# TF graphx = tf.placeholder(tf.float32, [None, 784])y = tf.placeholder(tf.float32, [None, 10])W = tf.Variable(tf.zeros([784, 10]))b = tf.Variable(tf.zeros([10]))pred = tf.nn.softmax(tf.matmul(x, W) + b)cost = tf.reduce_mean(-tf.reduce_sum(y*tf.log(pred), reduction_indices=1))optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))init = tf.global_variables_initializer()mnist = input_data.read_data_sets("data/mnist-tf", one_hot=True)train_x_all = mnist.train.imagestrain_y_all = mnist.train.labelstest_x = mnist.test.imagestest_y = mnist.test.labelsdef run_train(session, train_x, train_y):  print "\nStart training"  session.run(init)  for epoch in range(10):    total_batch = int(train_x.shape[0] / batch_size)    for i in range(total_batch):      batch_x = train_x[i*batch_size:(i+1)*batch_size]      batch_y = train_y[i*batch_size:(i+1)*batch_size]      _, c = session.run([optimizer, cost], feed_dict={x: batch_x, y: batch_y})      if i % 50 == 0:        print "Epoch #%d step=%d cost=%f" % (epoch, i, c)def cross_validate(session, split_size=5):  results = []  kf = KFold(n_splits=split_size)  for train_idx, val_idx in kf.split(train_x_all, train_y_all):    train_x = train_x_all[train_idx]    train_y = train_y_all[train_idx]    val_x = train_x_all[val_idx]    val_y = train_y_all[val_idx]    run_train(session, train_x, train_y)    results.append(session.run(accuracy, feed_dict={x: val_x, y: val_y}))  return resultswith tf.Session() as session:  result = cross_validate(session)  print "Cross-validation result: %s" % result  print "Test accuracy: %f" % session.run(accuracy, feed_dict={x: test_x, y: test_y})

As the dataset gets larger cross validation gets more expensive.In deep learning we usually use large data sets.You should be fine with simple training. Tensorflow doesnt have a built in mechanism for cv as it is not usually used in neural networks.In neural networks, the efficiency of the network relies mainly on the data set, number of epochs and the learning rate.

I have used cv in sklearnYou can check the link:https://github.com/hackmaster0110/Udacity-Data-Analyst-Nano-Degree-Projects/

In that,go to poi_id.py in Identify fraud from enron data(In Project folder)