text: import tensorflow as tf
  from tensorflow.examples.tutorials.mnist import input_data
  mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
  x = tf.placeholder("float",[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.initialize_all_variables()
  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, tf.float32))
      print(sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))