text: import tensorflow as tf
  import numpy as np
  import matplotlib.pyplot as plt
  
  def add_layer(inputs, in_size, out_size, n_layer, activation_function=None):
      # add one more layer and return the output of this layer
      layer_name = 'layer%s' %n_layer
      with tf.name_scope(layer_name):
          with tf.name_scope('weights'):
              Weights = tf.Variable(tf.random_normal([in_size,out_size]))
              tf.summary.histogram(layer_name+'/weights',Weights)
          with tf.name_scope('biases'):
              biases = tf.Variable(tf.zeros([1,out_size])+0.1)
              tf.summary.histogram(layer_name+'/biases',biases)
          with tf.name_scope('Wx_plus_b'):
              Wx_plus_b = tf.matmul(inputs,Weights) + biases
          if activation_function is None:
              outputs = Wx_plus_b
          else:
              outputs = activation_function(Wx_plus_b)
              tf.summary.histogram(layer_name+'/outputs',outputs)
          return outputs
  
  # make up some real data
  x_data = np.linspace(-1,1,300) [:,np.newaxis]
  noise = np.random.normal(0, 0.05, x_data.shape)
  y_data = np.square(x_data) - 0.5 + noise
  
  # define placeholder for inputs to network
  with tf.name_scope('inputs'):
      xs = tf.placeholder(tf.float32,[None,1], name = 'x_input')
      ys = tf.placeholder(tf.float32,[None,1], name = 'y_input')
  
  # add hidden layer
  layer1 = add_layer(xs, 1, 10, n_layer=1, activation_function=tf.nn.relu)
  # add output layer
  prediction = add_layer(layer1, 10, 1, n_layer=2, activation_function=None)
  
  # the error between prediction and real data
  with tf.name_scope('loss'):
      loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction),
                      reduction_indices=[1]))
      tf.summary.scalar('loss',loss)
  
  with tf.name_scope('train'):
      train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)
  
  init = tf.global_variables_initializer()
  sess = tf.Session()
  merged = tf.summary.merge_all()
  sess.run(init)
  writer = tf.summary.FileWriter("logs/", sess.graph)
  
  fig = plt.figure()
  axis = fig.add_subplot(1,1,1)
  axis.scatter(x_data,y_data)
  plt.ion()
  plt.show()
  for i in range(1000):
      sess.run(train_step,feed_dict={xs:x_data,ys:y_data})
      if i % 50 == 0:
          # see the step improvement
          # print(sess.run(loss,feed_dict={xs:x_data,ys:y_data}))
          try:
              axis.lines.remove(lines[0])
          except Exception:
              pass
          prediction_value = sess.run(prediction, feed_dict={xs:x_data})
          result = sess.run(merged, feed_dict={xs:x_data, ys:y_data})
          lines = axis.plot(x_data,prediction_value, 'r-', lw=5)
          plt.pause(0.1)
          writer.add_summary(result, i)
  