text: from tensorflow.examples.tutorials.mnist import input_data
  mnist = input_data.read_data_sets("MNIST_data/", one_hot = True)
  import tensorflow as tf
  
  if __name__ == '__main__':
      
      x = tf.placeholder(tf.float32, [None,784], name="x")
  
      W = tf.Variable(tf.zeros([784,10]))
      b = tf.Variable(tf.zeros([10]))
  
      y = tf.nn.softmax(tf.matmul(x,W) + b, name="y")
  
      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)
  
      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}))
  
      saver = tf.train.Saver()
  
      saver.save(sess, "model/deep_mnist_model")
  