text: import tensorflow as tf
  from tensorflow.examples.tutorials.mnist import input_data
  # number 1 to 10 data
  mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
  
  def compute_accuarcy(v_xs, v_ys):
      global prediction
      y_pre = sess.run(prediction, feed_dict={xs:v_xs, keep_prob: 1})
      correct_prediction = tf.equal(tf.argmax(y_pre, 1), tf.argmax(v_ys, 1))
      accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
      result = sess.run(accuracy, feed_dict={xs:v_xs, ys:v_ys, keep_prob: 1})
      return result
  
  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):
      # stride=[1, x_movement, y_movement, 1]
      # Must have strides[0] = strides[3] = 1
      return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
  
  def max_pool_2x2(x):
      # stride=[1, x_movement, y_movement, 1]
      # Must have strides[0] = strides[3] = 1
      return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding = 'SAME')
  
  # define placeholder for inputs to network
  xs = tf.placeholder(tf.float32, [None, 784]) / 255 # 28*28
  ys = tf.placeholder(tf.float32, [None, 10])
  keep_prob = tf.placeholder(tf.float32)
  x_image = tf.reshape(xs, [-1, 28, 28, 1])
  # print(x_image.shape) # [n_samples, 28, 28, 1]
  
  ## conv1 layer ##
  W_conv1 = weight_variable([5, 5, 1, 32]) # patch 5*5, in size 1, out size 32
  b_conv1 = bias_variable([32])
  h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) # output size 28*28*32
  h_pool1 = max_pool_2x2(h_conv1)                                       # output size 14*14*32
  ## conv2 layer ##
  W_conv2 = weight_variable([5, 5, 32, 64]) # patch 5*5, in size 32, out size 64
  b_conv2 = bias_variable([64])
  h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2) # output size 14*14*64
  h_pool2 = max_pool_2x2(h_conv2)                                       # output size 7*7*64
  
  ## fuc1 layer ##
  W_func1 = weight_variable([7*7*64, 1024])
  b_func1 = bias_variable([1024])
  h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64]) # [n_samples, 7, 7, 64] >> [n_samples, 7*7*64]
  h_func1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_func1) + b_func1)
  h_func1_drop = tf.nn.dropout(h_func1, keep_prob)
  ## fuc2 layer ##
  W_func2 = weight_variable([1024, 10])
  b_func2 = bias_variable([10])
  prediction = tf.nn.softmax(tf.matmul(h_func1_drop, W_func2) + b_func2)
  
  # the error between prediction and real data
  cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys * tf.log(prediction),
                                                reduction_indices=[1]))       # loss
  train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
  
  sess = tf.Session()
  
  # important step
  sess.run(tf.global_variables_initializer())
  
  for i in range(1000):
      batch_xs, batch_ys = mnist.train.next_batch(100)
      sess.run(train_step, feed_dict={xs: batch_xs, ys: batch_ys, keep_prob: 0.5})
      if i % 50 == 0:
          print(compute_accuracy(
              mnist.test.images, mnist.test.lables))
  