new_contents: from tensorflow.examples.tutorials.mnist import input_data
  mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
  
  import tensorflow as tf
  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(tf.float32, [None, 10])
  
  cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))
  train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
  
  sess = tf.InteractiveSession()
  tf.global_variables_initializer().run()
  
  for _ 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}))
  
subject: Create tf-mnist-softmax.py