TensorFlow是否为其用户实现了交叉验证？

Answer 1

Another option you would have with sklearn is:

sklearn.model_selection.train_test_split(*arrays, **options)

用法示例：

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)

将数组或矩阵X和y分成随机列并测试大小为42的子集。

Answer 2

如前所述，tensorflow没有提供自己的方法来交叉验证模型。建议使用^{}。这有点乏味，但可行。下面是一个完整的交叉验证MNIST模型示例，其中包含tensorflow和KFold：

from sklearn.model_selection import KFold
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

# Parameters
learning_rate = 0.01
batch_size = 500

# TF graph
x = 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.images
train_y_all = mnist.train.labels
test_x = mnist.test.images
test_y = mnist.test.labels

def 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 results

with 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})

Answer 3

随着数据集越来越大，交叉验证变得越来越昂贵。在深入学习中，我们通常使用大型数据集。您应该接受简单的培训。Tensorflow没有一个用于cv的内置机制，因为它通常不用于神经网络，在神经网络中，网络的效率主要依赖于数据集、时段数和学习率。

我在sklearn用过简历 您可以检查链接： https://github.com/hackmaster0110/Udacity-Data-Analyst-Nano-Degree-Projects/

在这篇文章中，请转到“识别安然数据中的欺诈”中的poi_id.py（在项目文件夹中）