TensorFlow中with as语句的使用问题

Question

A session may own resources, such as variables, queues, and readers. It is important to release these resources when they are no longer required.

with as语句在资源不再使用后才能使用，但是我在mnist初步入门的那个代码中使用后却报了一大堆错误，这是为啥？

import tensorflow.examples.tutorials.mnist.input_data as input_data
import tensorflow as tf

mnist = input_data.read_data_sets('MNIST_data/',one_hot = True)
#mnist.train
#mnist.test
#mnist.train.images [60000,784]
#mnist.train.labels [60000,10]

x = tf.placeholder(tf.float32,[None,784])

w = tf.Variable(tf.zeros([784,10]))
b = tf.Variable(tf.zeros([10]))

y = tf.nn.softmax(tf.matmul(x,w)+b)

y_ = tf.placeholder("float",[None,10])

cross_entropy = -tf.reduce_sum(y_*tf.log(y))

train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)

init = tf.global_variables_initializer()

sess = tf.Session()
sess.run(init)

for i in range(1000):
batch_xs,batch_ys = mnist.train.next_batch(100)

sess.run(train_step,feed_dict = {x:batch_xs,y_:batch_ys})

correct_prediction = tf.equal(tf.argmax(y,1),tf.argmax(y_,1))

accuracy = tf.reduce_mean(tf.cast(correct_prediction,"float"))

output = sess.run(accuracy, feed_dict={x: mnist.test.images, y: mnist.test.labels})
print(output)
sess.close()
_
斜体部分 我用with tf.Session() as sess:…………代替后报错了，
是不是因为之前用了sess = tf.Sesssion()的缘故？？
还有 ， 像在上面代码中多次用到sess.run()，是不是不能随便用with as语句了呢？

Answer 1

是这样的，with tf.Session() as sess语句相当于在这段代码开始时执行sess=tf.Session()，在结束时执行sess.close()
因此正确的写法应该是这样的

import tensorflow.examples.tutorials.mnist.input_data as input_data
import tensorflow as tf

mnist = input_data.read_data_sets('MNIST_data/',one_hot = True)
#mnist.train
#mnist.test
#mnist.train.images [60000,784]
#mnist.train.labels [60000,10]

x = tf.placeholder(tf.float32,[None,784])

w = tf.Variable(tf.zeros([784,10]))
b = tf.Variable(tf.zeros([10]))

y = tf.nn.softmax(tf.matmul(x,w)+b)

y_ = tf.placeholder("float",[None,10])

cross_entropy = -tf.reduce_sum(y_*tf.log(y))

train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)

init = tf.global_variables_initializer()

with tf.Session() as sess:
    sess.run(init)

    for i in range(1000):
        batch_xs,batch_ys = mnist.train.next_batch(100)
        sess.run(train_step,feed_dict = {x:batch_xs,y_:batch_ys})

    correct_prediction = tf.equal(tf.argmax(y,1),tf.argmax(y_,1))

    accuracy = tf.reduce_mean(tf.cast(correct_prediction,"float"))

        # 另外，此处你的feed_dict写错了，应该为y_，而不是y
    output = sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels})
    print(output)