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
  sess = tf.InteractiveSession()
  
  x = tf.placeholder(tf.float32, shape=[None, 784])
  y_ = tf.placeholder(tf.float32, shape=[None, 10])
  
  W = tf.Variable(tf.zeros([784,10]))
  b = tf.Variable(tf.zeros([10]))
  
  
  sess.run(tf.global_variables_initializer())
  
  y = tf.matmul(x,W) + b
  
  cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(y, y_))
  
  train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
  
  for i in range(1000):
  	batch = mnist.train.next_batch(100)
  	train_step.run(feed_dict={x: batch[0], y_: batch[1]})
  
  correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
  
  accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
  
  print(accuracy.eval(feed_dict={x: mnist.test.images, y_: mnist.test.labels}))
  
  
  ## Multilayer Convolutional Network
  
  def weight_variable(shape):
  	initial = tf.truncated_normal(shape, stddev=0.1)
  	return tf.Variable(initial)
  
  def bias_variable(shape):
  	initial = tf.constant(0.1, shape=shape)
  	return tf.Variable(initial)
  
  def conv2d(x, W):
  	return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
  
  def max_pool_2x2(x):
  	return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
  	                    strides=[1, 2, 2, 1], padding='SAME')
  
  
  W_conv1 = weight_variable([5, 5, 1, 32])
  b_conv1 = bias_variable([32])
  
  x_image = tf.reshape(x, [-1,28,28,1])
  
  h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
  h_pool1 = max_pool_2x2(h_conv1)
  
  
  W_conv2 = weight_variable([5, 5, 32, 64])
  b_conv2 = bias_variable([64])
  
  h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
  h_pool2 = max_pool_2x2(h_conv2)
  
  W_fc1 = weight_variable([7 * 7 * 64, 1024])
  b_fc1 = bias_variable([1024])
  
  h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
  h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
  
  keep_prob = tf.placeholder(tf.float32)
  h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
  
  W_fc2 = weight_variable([1024, 10])
  b_fc2 = bias_variable([10])
  
  y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2
  
  
  cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(y_conv, y_))
  train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
  correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1))
  accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
  sess.run(tf.global_variables_initializer())
  for i in range(20000):
  	batch = mnist.train.next_batch(50)
  	if i%100 == 0:
  		train_accuracy = accuracy.eval(feed_dict={
  			x:batch[0], y_: batch[1], keep_prob: 1.0})
  		print("step %d, training accuracy %g"%(i, train_accuracy))
  	train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})
  
  print("test accuracy %g"%accuracy.eval(feed_dict={
      x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))
  
  
subject: add multilayer cnn